AI LAB PROJECT¶
A.A.:2023/2024
In the following study, we are going to analyze four distinct dataset containing information about two different cancerous cells (MCF7 and HCC1806) studied through two different sequencing techniques (SMART and DROP). The goal of the study is to build different predictors capable of classifying each cell as either hypoxic or normoxic looking at its genes given by the sequencing: in order to do so, we are going to build different predictors and analyze their individual performances, so that we can understand which classifier operates with the highest accuracy and whether some predictors perform optimally only in a specific setting.
With this goal in mind, we are going to proceed in the following way: first, we are going to perform an exploratory data analysis on the four datasets, which have been given us both in the original version and in a filtered and normalized version; the analysis will include clustering, dimensionality reduction and sampling of mean and variance of the genes, in order to have a basic understanding of which genes are more relevant and how the datasets have been filtered. Afterwards, we will focus on the study of different supervised learning techniques, trying to identify the best predictor for our scope.
MAP OF THE REPORT¶
0: importing libraries
1: EDA
1.0: importing
1.1: MCF7
- 1.1.1 READING THE META-DATASET USEFUL SINCE CONTAINS SOME INSIGHTS
- 1.1.2 OPENING THE MAIN DATASET: UNFILTERED MCF7 DATASET PRODUCED USING THE SMART SEQUENCING TECHNOLOGY
- 1.1.3 CHECKING MISSING AND NON VALID VALUES IN OUR DATASET
- 1.1.4 COMPUTATION AND VISUALIZATION OF MEAN, STD, QUANTILES, MAX AND MIN
- 1.1.5 CHECK OF OUTLIERS
- 1.1.6 SPARSITY OF DATA
- 1.1.7 DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS
- 1.1.8 CHECK OF NORMALIZATION OF DATA USING SKEWENESS AND KURTOSIS
- 1.1.9 DATA TRANSORMATIONS USING LOG 2
1.2 HCC1806
- 1.2.1 READING THE META-DATASET USEFUL SINCE CONTAINS SOME INSIGHTS
- 1.2.2 OPENING THE MAIN DATASET: UNFILTERED MCF7 DATASET PRODUCED USING THE SMART SEQUENCING TECHNOLOGY
- 1.2.3 CHECKING MISSING AND NON VALID VALUES IN OUR DATASET
- 1.2.4 COMPUTATION AND VISUALIZATION OF MEAN, STD, QUANTILES, MAX AND MIN
- 1.2.5 CHECK OF OUTLIERS
- 1.2.6 SPARSITY OF DATA
- 1.2.7 DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS
- 1.2.8 CHECK OF NORMALIZATION OF DATA USING SKEWENESS AND KURTOSIS
- 1.2.9 DATA TRANSORMATIONS USING LOG 2
2: Analysis on unfiltered/filtered Smartseq and Unsupervided/Supervised learning on SmartSeq
- 2.1 MCF7
- 2.1.0 Importing datasets
- 2.1.1 Statistical study on mean and variances on cells
- 2.1.1.1 Study of mean on cells
- 2.1.1.2 Study of STD on cells
- 2.1.1.3 DETAILS OF DISTRIBUTION OF MEANS
- 2.1.1.4 DETAILS OF DISTRIBUTION OF STD
- 2.1.2 Study on genes by mean and variances on genes
- 2.1.2.1 Study of mean on cells
- 2.1.2.2 Study of STD on cells
- 2.1.2.3 DETAILS OF DISTRIBUTION OF MEANS
- 2.1.2.4 DETAILS OF DISTRIBUTION OF STD
- 2.1.3 TRIAL AND ERROR OF PSEUDO FILTERING ON CELLS
- 2.1.4 TRIAL AND ERROR OF PSEUDO FILTERING ON GENES
- 2.1.4bis Merging the two pseudo filtering approaches
- 2.1.5 PCA
- 2.1.6 Clustering
- 2.1.6.1 Single linkage
- 2.1.6.2 Average linkage
- 2.1.6.3 Complete Linkage
- 2.1.7 Unsupervised learning applied to MCF7
- 2.1.8 Looking for most useful and expressed genes across cells
- 2.1.9 More unsupervised learning
- 2.2 HCC1806
- 2.2.0 Importing datasets
- 2.2.1 Statistical study on mean and variances on cells
- 2.2.1.1 Study of mean on cells
- 2.2.1.2 Study of STD on cells
- 2.2.1.3 DETAILS OF DISTRIBUTION OF MEANS
- 2.2.1.4 DETAILS OF DISTRIBUTION OF STD
- 2.2.2 Study on genes by mean and variances on genes
- 2.2.2.1 Study of mean on cells
- 2.2.2.2 Study of STD on cells
- 2.2.2.3 DETAILS OF DISTRIBUTION OF MEANS
- 2.2.2.4 DETAILS OF DISTRIBUTION OF STD
- 2.2.3 TRIAL AND ERROR OF PSEUDO FILTERING ON CELLS
- 2.2.4 TRIAL AND ERROR OF PSEUDO FILTERING ON GENES
- 2.2.4bis Merging the two pseudo filtering approaches
- 2.2.5 PCA
- 2.2.6 Clustering
- 2.2.6.1 Single linkage
- 2.2.6.2 Average linkage
- 2.2.6.3 Complete Linkage
- 2.2.7 Unsupervised learning applied to MCF7
- 2.2.8 Looking for most useful and expressed genes across cells
- 2.2.9 More unsupervised learning
- 2.1 MCF7
3: Analysis on DropSeq and Unsupervided/Supervised learning on DropSeq
- 3.1 MCF7
- 3.1.0 OPENING THE MAIN DATASET: MCF7 DATASET PRODUCED USING THE DROPSEQ TECHNOLOGY
- 3.1.1 SPARSITY OF THE DATA
- 3.1.2 DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS
- 3.1.3 LOOKING FOR MOST USEFUL AND EXPRESSED GENES ACROSS CELLS
- 3.1.4 UNSUPERVISD LEARNING APPLIED TO MCF7
- 3.1.5 More unsupervised learning
- 3.2 HCC1806
- 3.2.0 OPENING THE MAIN DATASET: HCC1806 DATASET PRODUCED USING THE DROPSEQ TECHNOLOGY
- 3.2.1 SPARSITY OF THE DATA
- 3.2.2 DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS
- 3.2.3 LOOKING FOR MOST USEFUL AND EXPRESSED GENES ACROSS CELLS
- 3.2.4 UNSUPERVISD LEARNING APPLIED TO HCC1806
- 3.2.5 More unsupervised learning
- 3.1 MCF7
4: Classifiers
4.0 Definitions
- 4.0.1 Definition of Neural Networks
- 4.0.1.1 Definition of the neural network on single dataset (dropseq)
- 4.0.1.2 Definition on merged dropseq
- 4.0.1.3 Definition on general (all 4 dataset merged)
- 4.0.2 Definition of KNN
- 4.0.3 Definition Logistic Regression
- 4.0.4 Definition of CatBoost
- 4.0.1 Definition of Neural Networks
4.1 PREDICTORS SMARTSEQ MCF7
- 4.1.1 SVM
- 4.1.2 Random Forest
- 4.1.3 XGBoost
- 4.1.4 KNN
- 4.1.5 Logistic Regression
- 4.1.6 CatBoost
4.2 PREDICTORS SMARTSEQ HCC1806
- 4.2.1 SVM
- 4.2.2 Random Forest
- 4.2.3 XGBoost
- 4.2.4 KNN
- 4.2.5 Logistic Regression
- 4.2.6 CatBoost
4.3 PREDICTORS DROPSEQ MCF7
- 4.3.1 SVM
- 4.3.2 Random Forest
- 4.3.3 XGBoost and ADABoost
- 4.3.4 KNN
- 4.3.5 Logistic Regression
- 4.3.6 CatBoost
4.4 PREDICTORS DROPSEQ HCC1806
- 4.4.1 SVM
- 4.4.2 Random Forest
- 4.4.3 XGBoost
- 4.4.4 KNN
- 4.4.5 Logistic Regression
- 4.4.6 CatBoost
4.5 PREDICTORS ON DROPSEQ MERGED (Neural Network)
4.6 PREDICTORS ON ALL DATASET MERGED
- 4.6.1 SVM
- 4.6.2 Random Forest
- 4.6.3 XGBoost
- 4.6.4 KNN
- 4.6.5 Logistic Regression
- 4.6.6 CatBoost
5: Voting Classifiers
- 5.1 SVM
- 5.2 XGB
- 5.3 KNN
- 5.4 Logistic Regression
- 5.5 CatBoost
6: Predictions
- 6.1 Prediction on SmartSeq MCF7
- 6.1.0 Loading dataset Start by opening the dataset "MCF7_SmartS_Filtered_Normalised_3000_Data_test_anonim"
- 6.1.1 Random Forest
- 6.1.2 Logistic Regression
- 6.1.3 SVM
- 6.1.4 Final considerations
- 6.2 Prediction on SmartSeq HCC1806
- 6.2.1 SVM
- 6.2.2 CatBoost
- 6.2.3 Logistic Regression
- 6.2.4 Final considerations
- 6.3 Prediction on DropSeq MCF7
- 6.3.0 Start by opening the dataset "MCF7_Filtered_Normalised_3000_Data_test_anonim"
- 6.3.1 NN
- 6.3.2 CatBoost
- 6.3.3 Logistic Regression
- 6.3.4 Final considerations
- 6.4 Prediction on DropSeq HCC1806
- 6.4.0 Start by opening the dataset "HCC1806_Filtered_Normalised_3000_Data_test_anonim"
- 6.4.1 NN
- 6.4.2 SVM
- 6.4.3 Logistic Regression
- 6.4.4 Final conclusions
- 6.1 Prediction on SmartSeq MCF7
¶
- importing libraries
import sys
import sklearn
import pandas as pd
import numpy as np
from numpy import count_nonzero
import seaborn as sns
from scipy import sparse
from scipy.sparse import lil_matrix
import re
from scipy.cluster.hierarchy import dendrogram
#%matplotlib inline
sns.set(color_codes=True)
import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans , AgglomerativeClustering, DBSCAN
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.model_selection import cross_val_score,StratifiedKFold, train_test_split,LeaveOneOut, GridSearchCV, KFold
from sklearn.svm import SVC, NuSVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, f1_score
import xgboost
from xgboost import XGBClassifier
import catboost
from catboost import CatBoostClassifier, Pool, cv
from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.neural_network import MLPClassifier
¶
- Exploratory Data Analysis
¶
1.0) Importing
mcf_smartseq=pd.read_csv("SmartSeq\MCF7_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ", engine='python',index_col=0)
mcf_dropseq=pd.read_csv("DropSeq\MCF7_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ", engine='python',index_col=0)
hcc_smartseq=pd.read_csv("SmartSeq\HCC1806_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ", engine='python',index_col=0)
hcc_dropseq=pd.read_csv("DropSeq\HCC1806_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ", engine='python',index_col=0)
df_MCF7_SMART=mcf_smartseq.T
df_MCF7_DROP=mcf_dropseq.T
df_HCC1806_SMART=hcc_smartseq.T
df_HCC1806_DROP=hcc_dropseq.T
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
def prepare_data(df):
X = df.drop('TARGET', axis=1)
y = df['TARGET']
# train test samples
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# scaler applying
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
return X_train_scaled, X_test_scaled, y_train, y_test,scaler
df_MCF7_SMART.loc[df_MCF7_SMART.index.str.contains('Norm'), 'TARGET'] = 0
df_MCF7_SMART.loc[df_MCF7_SMART.index.str.contains('Hypo'), 'TARGET'] = 1
df_MCF7_DROP.loc[df_MCF7_DROP.index.str.contains('Norm'), 'TARGET'] = 0
df_MCF7_DROP.loc[df_MCF7_DROP.index.str.contains('Hypo'), 'TARGET'] = 1
df_HCC1806_SMART.loc[df_HCC1806_SMART.index.str.contains('Norm'), 'TARGET'] = 0
df_HCC1806_SMART.loc[df_HCC1806_SMART.index.str.contains('Hypo'), 'TARGET'] = 1
df_HCC1806_DROP.loc[df_HCC1806_DROP.index.str.contains('Norm'), 'TARGET'] = 0
df_HCC1806_DROP.loc[df_HCC1806_DROP.index.str.contains('Hypo'), 'TARGET'] = 1
X_train_HCC1806_DROP, X_test_HCC1806_DROP, y_train_HCC1806_DROP, y_test_HCC1806_DROP = prepare_data(df_HCC1806_DROP)
X_train_HCC1806_SMART, X_test_HCC1806_SMART, y_train_HCC1806_SMART, y_test_HCC1806_SMART = prepare_data(df_HCC1806_SMART)
X_train_MCF7_DROP, X_test_MCF7_DROP, y_train_MCF7_DROP, y_test_MCF7_DROP = prepare_data(df_MCF7_DROP)
X_train_MCF7_SMART, X_test_MCF7_SMART, y_train_MCF7_SMART, y_test_MCF7_SMART = prepare_data(df_MCF7_SMART)
df_basic_HCC_SMART=hcc_smartseq
df_trans_HCC_SMART=df_basic_HCC_SMART.T
df_trans_HCC_SMART['TARGET'] = df_trans_HCC_SMART.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_HCC_SMART.loc[df_trans_HCC_SMART.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_HCC_SMART.loc[df_trans_HCC_SMART.index.str.contains('Hypo'), 'TARGET'] = 1
X_train_HCC_SMART, X_test_HCC_SMART, y_train_HCC_SMART, y_test_HCC_SMART,scaler_HCC_SMART = prepare_data(df_trans_HCC_SMART)
df_basic_MCF_SMART=mcf_smartseq
df_trans_MCF_SMART=df_basic_MCF_SMART.T
df_trans_MCF_SMART['TARGET'] = df_trans_MCF_SMART.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_MCF_SMART.loc[df_trans_MCF_SMART.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_MCF_SMART.loc[df_trans_MCF_SMART.index.str.contains('Hypo'), 'TARGET'] = 1
X_train_MCF_SMART, X_test_MCF_SMART, y_train_MCF_SMART, y_test_MCF_SMART,scaler_MCF_SMART = prepare_data(df_trans_MCF_SMART)
df_basic_hcc_drop=hcc_dropseq
df_trans_hcc_drop=df_basic_hcc_drop.T
df_trans_hcc_drop['TARGET'] = df_trans_hcc_drop.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_hcc_drop.loc[df_trans_hcc_drop.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_hcc_drop.loc[df_trans_hcc_drop.index.str.contains('Hypo'), 'TARGET'] = 1
X_train_hcc_drop, X_test_hcc_drop, y_train_hcc_drop, y_test_hcc_drop,scaler_hcc_drop = prepare_data(df_trans_hcc_drop)
df_basic_MCF7_DROP=mcf_dropseq
df_trans_MCF7_DROP=df_basic_MCF7_DROP.T
df_trans_MCF7_DROP['TARGET'] = df_trans_MCF7_DROP.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_MCF7_DROP.loc[df_trans_MCF7_DROP.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_MCF7_DROP.loc[df_trans_MCF7_DROP.index.str.contains('Hypo'), 'TARGET'] = 1
X_train_MCF7_DROP, X_test_MCF7_DROP, y_train_MCF7_DROP, y_test_MCF7_DROP,scaler_MCF7_DROP = prepare_data(df_trans_MCF7_DROP)
¶
1.1) MCF7
¶
1.1.1) READING THE META-DATASET USEFUL SINCE CONTAINS SOME INSIGHTS
Firstly we download and open the meta_data file of MCF7 since it provides important information about how the project was conducted.
df_MCF7_unf_meta = pd.read_csv("SmartSeq/MCF7_SmartS_MetaData.tsv",delimiter="\t",engine='python',index_col=0)
print("Dataframe dimensions:", np.shape(df_MCF7_unf_meta))
print("First column: ", df_MCF7_unf_meta.iloc[ : , 0])
Dataframe dimensions: (383, 8)
First column: Filename
output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam MCF7
output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam MCF7
output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam MCF7
output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam MCF7
output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam MCF7
...
output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam MCF7
output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam MCF7
output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam MCF7
output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam MCF7
output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam MCF7
Name: Cell Line, Length: 383, dtype: object
df_MCF7_unf_meta.shape
(383, 8)
As we can see the file has a shape of 383 rows and 8 columns and is made in the following way: the rows represent the cells while the columns give us information about the experiment, e.g. we have the cell line, the plate, the position of the cell, the condition (hypoxia or Normoxia), and other informations as the comment or tag.
Moreover the 383 cells tell us that in the main dataset we will have 383 cells (in that case as columns)
df_MCF7_unf_meta.head(243)
| Cell Line | Lane | Pos | Condition | Hours | Cell name | PreprocessingTag | ProcessingComments | |
|---|---|---|---|---|---|---|---|---|
| Filename | ||||||||
| output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam | MCF7 | output.STAR.1 | A10 | Hypo | 72 | S28 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam | MCF7 | output.STAR.1 | A11 | Hypo | 72 | S29 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam | MCF7 | output.STAR.1 | A12 | Hypo | 72 | S30 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam | MCF7 | output.STAR.1 | A1 | Norm | 72 | S1 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam | MCF7 | output.STAR.1 | A2 | Norm | 72 | S2 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| output.STAR.3_D8_Hypo_S182_Aligned.sortedByCoord.out.bam | MCF7 | output.STAR.3 | D8 | Hypo | 72 | S182 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.3_D9_Hypo_S183_Aligned.sortedByCoord.out.bam | MCF7 | output.STAR.3 | D9 | Hypo | 72 | S183 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.3_E10_Hypo_S232_Aligned.sortedByCoord.out.bam | MCF7 | output.STAR.3 | E10 | Hypo | 72 | S232 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.3_E11_Hypo_S233_Aligned.sortedByCoord.out.bam | MCF7 | output.STAR.3 | E11 | Hypo | 72 | S233 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.3_E12_Hypo_S234_Aligned.sortedByCoord.out.bam | MCF7 | output.STAR.3 | E12 | Hypo | 72 | S234 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
243 rows × 8 columns
¶
1.1.2) OPENING THE MAIN DATASET: UNFILTERED MCF7 DATASET PRODUCED USING THE SMART SEQUENCING TECHNOLOGY
We start now opening the MCF7 SmartSeq unfiltered dataset since we are interested in analyzing it.
# The dataset unfiltered is one where we can apply statistical tools to extract valuable insights
df_MCF7_unf = pd.read_csv("SmartSeq/MCF7_SmartS_Unfiltered_Data.txt",delimiter="\ ",engine='python',index_col=0)
print("Dataframe dimensions:", np.shape(df_MCF7_unf))
print("First column: ", df_MCF7_unf.iloc[ : , :])
Dataframe dimensions: (22934, 383)
First column: "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 1
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 4
"MT-CYB" 270
"MT-TT" 0
"MT-TP" 5
"MAFIP" 8
"output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 0
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 0
"MT-CYB" 1
"MT-TT" 0
"MT-TP" 0
"MAFIP" 0
"output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 0
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 0
"MT-CYB" 76
"MT-TT" 0
"MT-TP" 0
"MAFIP" 0
"output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 0
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 0
"MT-CYB" 66
"MT-TT" 0
"MT-TP" 1
"MAFIP" 0
"output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 0
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 3
"MT-CYB" 727
"MT-TT" 0
"MT-TP" 0
"MAFIP" 0
"output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 1
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 3
"MT-CYB" 2717
"MT-TT" 1
"MT-TP" 1
"MAFIP" 0
"output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 10
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 0
"MT-CYB" 9326
"MT-TT" 1
"MT-TP" 1
"MAFIP" 0
"output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 1
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 0
"MT-CYB" 3253
"MT-TT" 1
"MT-TP" 4
"MAFIP" 1
"output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 0
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 14
"MT-CYB" 7949
"MT-TT" 4
"MT-TP" 2
"MAFIP" 0
"output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" ... \
"WASH7P" 0 ...
"MIR6859-1" 0 ...
"WASH9P" 0 ...
"OR4F29" 0 ...
"MTND1P23" 0 ...
... ... ...
"MT-TE" 1 ...
"MT-CYB" 30 ...
"MT-TT" 0 ...
"MT-TP" 0 ...
"MAFIP" 0 ...
"output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 1
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 0
"MT-CYB" 239
"MT-TT" 0
"MT-TP" 0
"MAFIP" 0
"output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 1
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 4
"MT-CYB" 3795
"MT-TT" 7
"MT-TP" 14
"MAFIP" 0
"output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 0
"OR4F29" 0
"MTND1P23" 1
... ...
"MT-TE" 12
"MT-CYB" 12761
"MT-TT" 4
"MT-TP" 56
"MAFIP" 0
"output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" \
"WASH7P" 1
"MIR6859-1" 0
"WASH9P" 0
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 4
"MT-CYB" 2263
"MT-TT" 2
"MT-TP" 11
"MAFIP" 2
"output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 0
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 0
"MT-CYB" 1368
"MT-TT" 0
"MT-TP" 2
"MAFIP" 1
"output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 0
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 1
"MT-CYB" 570
"MT-TT" 0
"MT-TP" 0
"MAFIP" 0
"output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 1
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 6
"MT-CYB" 3477
"MT-TT" 3
"MT-TP" 7
"MAFIP" 6
"output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" \
"WASH7P" 1
"MIR6859-1" 0
"WASH9P" 1
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 0
"MT-CYB" 349
"MT-TT" 0
"MT-TP" 2
"MAFIP" 0
"output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"MIR6859-1" 0
"WASH9P" 4
"OR4F29" 1
"MTND1P23" 0
... ...
"MT-TE" 7
"MT-CYB" 2184
"MT-TT" 2
"MT-TP" 28
"MAFIP" 1
"output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"
"WASH7P" 1
"MIR6859-1" 0
"WASH9P" 5
"OR4F29" 0
"MTND1P23" 0
... ...
"MT-TE" 4
"MT-CYB" 1149
"MT-TT" 2
"MT-TP" 11
"MAFIP" 4
[22934 rows x 383 columns]
df_MCF7_unf.head()
| "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "WASH7P" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 |
| "MIR6859-1" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "WASH9P" | 1 | 0 | 0 | 0 | 0 | 1 | 10 | 1 | 0 | 0 | ... | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 4 | 5 |
| "OR4F29" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| "MTND1P23" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| "CPLANE2" | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | ... | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "MT1XP1" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "FBXO42" | 159 | 0 | 491 | 0 | 10 | 5 | 120 | 1 | 106 | 9 | ... | 58 | 0 | 0 | 23 | 19 | 78 | 7 | 683 | 389 | 286 |
| "SZRD1" | 65 | 0 | 47 | 0 | 29 | 212 | 175 | 217 | 219 | 2 | ... | 28 | 39 | 4 | 69 | 74 | 45 | 196 | 101 | 98 | 180 |
| "SPATA21" | 12 | 0 | 0 | 0 | 2 | 36 | 46 | 52 | 63 | 0 | ... | 12 | 9 | 0 | 26 | 18 | 6 | 75 | 21 | 30 | 54 |
243 rows × 383 columns
df_MCF7_unf.shape # shape of our unfiltered MCF7 SmartSeq dataset
(22934, 383)
The MCF7 SmartSeq unfiltered dataset is made of 22934 rows representing specific genes and 383 columns representing the cells (already available in the meta dataset). Therefore each entry tells us how much of every gene is expressed in every cells. Moreover, each cell has in its name written the state in which it was under (normoxia / hypoxia), and such information was given as a separate column in the meta dataset.
#We return the indexes of the dataframe, which are the gene symbols
gene_symbls_MCF7 = df_MCF7_unf.index
print("Dataframe indexes: ", gene_symbls_MCF7)
Dataframe indexes: Index(['"WASH7P"', '"MIR6859-1"', '"WASH9P"', '"OR4F29"', '"MTND1P23"',
'"MTND2P28"', '"MTCO1P12"', '"MTCO2P12"', '"MTATP8P1"', '"MTATP6P1"',
...
'"MT-TH"', '"MT-TS2"', '"MT-TL2"', '"MT-ND5"', '"MT-ND6"', '"MT-TE"',
'"MT-CYB"', '"MT-TT"', '"MT-TP"', '"MAFIP"'],
dtype='object', length=22934)
¶
1.1.3) CHECKING MISSING AND NON VALID VALUES IN OUR DATASET
We now perform some operations to see if there are non numeric or non valid types in our dataset.
# We print the data types of the columns
print("Data types: ", df_MCF7_unf.dtypes)
Data types: "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" int64
"output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" int64
"output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" int64
"output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" int64
"output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" int64
...
"output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" int64
"output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" int64
"output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" int64
"output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" int64
"output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" int64
Length: 383, dtype: object
# We create a dataframe from the original one with just the non-numeric columns to see if there are indeed non numeric values
non_numeric_df__MCF7_unf = df_MCF7_unf.select_dtypes(exclude=['int64', 'float64'])
# Print the data types of the non-numeric columns
print("Non-numeric data types: ", non_numeric_df__MCF7_unf.dtypes)
Non-numeric data types: Series([], dtype: object)
Apart of numeric values, there are not other types of values in our dataset.
# We now perform an additional step to explicitly check for NaN values in the dataframe
nan_df_MCF7_unf = df_MCF7_unf.isna()
# Print the columns that contain NaN values
print("Columns with NaN values: ", nan_df_MCF7_unf.any())
Columns with NaN values: "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" False
"output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" False
"output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" False
"output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" False
"output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" False
...
"output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" False
"output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" False
"output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" False
"output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" False
"output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" False
Length: 383, dtype: bool
print(df_MCF7_unf.isnull().sum()) # we return the sum of null values in our dataset
"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" 0
"output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" 0
"output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" 0
"output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" 0
"output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" 0
..
"output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" 0
"output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" 0
"output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" 0
"output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" 0
"output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" 0
Length: 383, dtype: int64
We see that the sum of all null value in our dataset for each cell is zero, therefore we don't need to do any additional change to our dataset.
# Also trying to drop the rows with missing values (but we see again that there are not missing values since the shape of the dataset is the same)
df_MCF7_unf = df_MCF7_unf.dropna()
df_MCF7_unf.shape
(22934, 383)
df_MCF7_unf_without_duplicates = df_MCF7_unf.drop_duplicates()
df_MCF7_unf_without_duplicates.shape
(22905, 383)
Checking for duplicates we get a new dataframe with a shape of (22905, 383) that is different to the one of the original dataset (22934, 383). This seems to suggest that there are indeed duplicates in our dataset
¶
1.1.4) COMPUTATION AND VISUALIZATION OF MEAN, STD, QUANTILES, MAX AND MIN
We now look at some information in our unfiltered dataset. We start using the describe method which returns for each cells the mean, std, the 25,50,75 quantiles based on the genes
# We start applying descriptive statistics to our dataset using the method .describe()
df_MCF7_unf_summary1 = df_MCF7_unf.describe()
df_MCF7_unf_summary1
| "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | ... | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 |
| mean | 40.817651 | 0.012253 | 86.442400 | 1.024636 | 14.531351 | 56.213613 | 75.397183 | 62.767725 | 67.396747 | 2.240734 | ... | 17.362562 | 42.080230 | 34.692422 | 32.735284 | 21.992718 | 17.439391 | 49.242784 | 61.545609 | 68.289352 | 62.851400 |
| std | 465.709940 | 0.207726 | 1036.572689 | 6.097362 | 123.800530 | 503.599145 | 430.471519 | 520.167576 | 459.689019 | 25.449630 | ... | 193.153757 | 256.775704 | 679.960908 | 300.291051 | 153.441647 | 198.179666 | 359.337479 | 540.847355 | 636.892085 | 785.670341 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 17.000000 | 0.000000 | 5.000000 | 0.000000 | 7.000000 | 23.000000 | 39.000000 | 35.000000 | 38.000000 | 1.000000 | ... | 9.000000 | 30.000000 | 0.000000 | 17.000000 | 12.000000 | 9.000000 | 27.000000 | 30.000000 | 38.000000 | 33.000000 |
| max | 46744.000000 | 14.000000 | 82047.000000 | 289.000000 | 10582.000000 | 46856.000000 | 29534.000000 | 50972.000000 | 36236.000000 | 1707.000000 | ... | 17800.000000 | 23355.000000 | 81952.000000 | 29540.000000 | 12149.000000 | 19285.000000 | 28021.000000 | 40708.000000 | 46261.000000 | 68790.000000 |
8 rows × 383 columns
We can start to see some interesting facts about our dataset:
- there are many cells that have the mean smaller then the maximum value (e.g in the first cell we can see the mean equal to almost 41 and the maximum value be 46744) and this seems to tell us that a lot of genes in our cells are not expressed while other are extremely characteristic.
- there are also some cells which have mean almost equal zero and this tells us that probably they don't have so much influence in the entire scenario (maybe because these cells will die or for other factors which are kind of domain questions)
Now we go deep into our analysis plotting the distribution of the mean in the cells with an histogram. The plot shows us that there are two main peaks: one between zero and ten, so there are a lot of cells with a mean of almost zero and therefore very few genes expressed, and another peak around 50. Therefore most cells have a mean of 0 or of 50.
Moreover the count of the means follow a bimodal distribution.
# Now let's try to represent through histograms the mean of the cells and how many times the different values appear
sns.displot(df_MCF7_unf_summary1.iloc[1], bins=70, color="green")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
We try to achieve the same plotting the distribution of the standard deviation in the cells with an histogram. We can see again two peaks, the first again around 0 and 10 and the second between 350 and 500. The standard deviation is not constant over the cells and there is also some dispersion in the dataset.
sns.displot(df_MCF7_unf_summary1.iloc[2], bins=70, color="skyblue")
plt.show()
# the cells with high std are the ones with high number of genes expressed
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Now we plot histograms of 25, 50, 75-percentile of the distribution of all the cells and we notice the following things:
- in the 25-percentile we have all cells with zero value (suggesting that 25% of genes in each cell has value equal zero)
- in the 50-percentule we have most cells with zero value (sugggesting that 50% of genes in each cell has value equal zero)
- in the 75% we have a peak at zero (suggesting most cells have 75% of genes with values equla zero), and another one around 30/40 (suggesting some cells have among the 75% of the genes with a value equal or smaller 30).
Therefore one hypothesis that can be formulated at this point is that the data we are analyzing now is sparse (so mostly formed by zero values) and not-symmetrical.
# See what happens with 75% quartile
sns.displot(data = df_MCF7_unf_summary1.iloc[6], bins=50, color="red")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
# See what happens with 25% quartile
sns.displot(data = df_MCF7_unf_summary1.iloc[4], bins=50, color="orange")
plt.show()
# at least 25% of the genes have a value of 0 in all cells, interesting
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
# See what happens with 50% quartile
sns.displot(data = df_MCF7_unf_summary1.iloc[5], bins=50, color="purple")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Finally, we represent the violin plot of all the cells (it is a little bit chaotic since there are many samples) to see how the distribution of each cell is concentrated.
Some of the samples have a distribution which is incredibly concentrated near zero (they are the ones with very short lines) while there are others represented by long line which have a wide range of gene expression levels, from very low to very high (in particular the length of these lines shows how far the most extreme values are from the median).
plt.figure(figsize=(16,4))
plot=sns.violinplot(data=df_MCF7_unf,palette="Set3",cut=0)
plt.show()
¶
1.1.5) CHECK OF OUTLIERS
We now try to find the outliers, eliminate them from the dataset and see what changes respect to the original situation
# Computations of interquartile range
Q1 = df_MCF7_unf.quantile(0.25)
Q3 = df_MCF7_unf.quantile(0.75)
IQR = Q3 - Q1
print(IQR)
"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" 17.0
"output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" 0.0
"output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" 5.0
"output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" 0.0
"output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" 7.0
...
"output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" 9.0
"output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" 27.0
"output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" 30.0
"output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" 38.0
"output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" 33.0
Length: 383, dtype: float64
df_MCF7_noOut = df_MCF7_unf[~((df_MCF7_unf < (Q1 - 1.5 * IQR)) |(df_MCF7_unf > (Q3 + 1.5 * IQR))).any(axis=1)]
df_MCF7_noOut.shape
(6435, 383)
df_MCF7_noOut.head(3)
| "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "MIR6859-1" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "CICP3" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "FAM87B" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 rows × 383 columns
We can notice that after having removed the outliers we have remained with a smaller dataset (precisely the shape is just of (6435,383)) and most of them are zero. The reason is because actually the genes carry most of information were the ones in the last quartile.
Therefore we now come back to the original situation and investigate the sparsity of the data.
¶
1.1.6) SPARSITY OF DATA
We now represent the dataframe as a simple matrix and look at how many zero we have. Then we compute the percentage of the sparsity on our dataset (we basically subtract to one the number_of_non_zero_genes/total_number of genes)
# We proceed in the following way. We start representing the dataframe as a simple matrix to see how many zero we have
# then we compute the percentage of the sparsity on our dataset doing 1 - number_of_non_zero_genes / total_number of genes
df_MCF7_unf_Matrix = np.array(df_MCF7_unf)
print("Matrix useful for the sparsity check: \n", df_MCF7_unf_Matrix)
Matrix useful for the sparsity check: [[ 0 0 0 ... 1 0 1] [ 0 0 0 ... 0 0 0] [ 1 0 0 ... 1 4 5] ... [ 0 0 0 ... 0 2 2] [ 5 0 0 ... 2 28 11] [ 8 0 0 ... 0 1 4]]
Sparsity_matrix_MCF7 = 1 - count_nonzero(df_MCF7_unf_Matrix)/ df_MCF7_unf_Matrix.size
Sparsity_matrix_MCF7
0.6021531646834907
As we mentioned before the dataframe is highly sparse, infact the percentage of sparsity is more then 60% of the total dataframe.
¶
1.1.7) DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS
We now repeat the same steps (representation of means, std, etc) using the dataset transposed in order to see which genes are highly expressed with respect to the others
df_MCF7_unf_transposed = df_MCF7_unf.transpose() # we transpose the dataset in order to get cells
# as rows and genes as columns
df_MCF7_unf_transposed
| "WASH7P" | "MIR6859-1" | "WASH9P" | "OR4F29" | "MTND1P23" | "MTND2P28" | "MTCO1P12" | "MTCO2P12" | "MTATP8P1" | "MTATP6P1" | ... | "MT-TH" | "MT-TS2" | "MT-TL2" | "MT-ND5" | "MT-ND6" | "MT-TE" | "MT-CYB" | "MT-TT" | "MT-TP" | "MAFIP" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" | 0 | 0 | 1 | 0 | 0 | 2 | 2 | 0 | 0 | 29 | ... | 0 | 0 | 0 | 505 | 147 | 4 | 270 | 0 | 5 | 8 |
| "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 12 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 76 | 0 | 0 | 0 |
| "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | ... | 1 | 0 | 0 | 44 | 8 | 0 | 66 | 0 | 1 | 0 |
| "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 68 | ... | 0 | 0 | 0 | 237 | 31 | 3 | 727 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 49 | ... | 0 | 0 | 1 | 341 | 46 | 1 | 570 | 0 | 0 | 0 |
| "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" | 0 | 0 | 1 | 0 | 0 | 2 | 5 | 5 | 0 | 370 | ... | 0 | 0 | 2 | 1612 | 215 | 6 | 3477 | 3 | 7 | 6 |
| "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" | 1 | 0 | 1 | 0 | 0 | 7 | 0 | 0 | 0 | 33 | ... | 0 | 0 | 0 | 62 | 20 | 0 | 349 | 0 | 2 | 0 |
| "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" | 0 | 0 | 4 | 1 | 0 | 29 | 4 | 0 | 0 | 228 | ... | 3 | 0 | 2 | 1934 | 575 | 7 | 2184 | 2 | 28 | 1 |
| "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" | 1 | 0 | 5 | 0 | 0 | 5 | 3 | 0 | 0 | 71 | ... | 5 | 2 | 3 | 1328 | 490 | 4 | 1149 | 2 | 11 | 4 |
383 rows × 22934 columns
df_MCF7_unf_summary2 = df_MCF7_unf_transposed.describe() # as before we applied the describe method to get the mean, std etc for each gene across all the cells
df_MCF7_unf_summary2
| "WASH7P" | "MIR6859-1" | "WASH9P" | "OR4F29" | "MTND1P23" | "MTND2P28" | "MTCO1P12" | "MTCO2P12" | "MTATP8P1" | "MTATP6P1" | ... | "MT-TH" | "MT-TS2" | "MT-TL2" | "MT-ND5" | "MT-ND6" | "MT-TE" | "MT-CYB" | "MT-TT" | "MT-TP" | "MAFIP" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 383.000000 | 383.000000 | 383.000000 | 383.00000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | ... | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.00000 | 383.000000 | 383.000000 | 383.000000 |
| mean | 0.133159 | 0.026110 | 1.344648 | 0.05483 | 0.049608 | 6.261097 | 4.681462 | 0.524804 | 0.073107 | 222.054830 | ... | 1.060052 | 0.443864 | 3.146214 | 1016.477807 | 204.600522 | 5.049608 | 2374.97389 | 2.083551 | 5.626632 | 1.749347 |
| std | 0.618664 | 0.249286 | 2.244543 | 0.31477 | 0.229143 | 7.565749 | 6.232649 | 0.980857 | 0.298131 | 262.616874 | ... | 1.990566 | 1.090827 | 4.265352 | 1009.444811 | 220.781927 | 6.644302 | 2920.39000 | 3.372714 | 7.511180 | 3.895204 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 23.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 172.000000 | 30.500000 | 0.000000 | 216.50000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 3.000000 | 2.000000 | 0.000000 | 0.000000 | 98.000000 | ... | 0.000000 | 0.000000 | 1.000000 | 837.000000 | 152.000000 | 3.000000 | 785.00000 | 0.000000 | 3.000000 | 0.000000 |
| 75% | 0.000000 | 0.000000 | 2.000000 | 0.00000 | 0.000000 | 10.000000 | 7.000000 | 1.000000 | 0.000000 | 370.500000 | ... | 1.000000 | 0.000000 | 5.000000 | 1549.000000 | 294.000000 | 7.000000 | 4059.00000 | 3.000000 | 8.000000 | 2.000000 |
| max | 9.000000 | 4.000000 | 20.000000 | 3.00000 | 2.000000 | 45.000000 | 36.000000 | 6.000000 | 2.000000 | 1662.000000 | ... | 15.000000 | 8.000000 | 22.000000 | 8115.000000 | 2002.000000 | 46.000000 | 16026.00000 | 22.000000 | 56.000000 | 32.000000 |
8 rows × 22934 columns
We can see that the mean of most genes is zero (therefore most genes are zero across all the cells), the std is zero as well in most of the genes, and the for the 75-percentile we have just few genes that are not zero in the 75 percentile. (this time we don't plot the 25/50-percentile since we would get again mainly zeros)
# We now plot more or less the same graph of before to see the expression of individual genes across cells
sns.displot(df_MCF7_unf_summary2.iloc[1], bins=600, color="green")
plt.xlim(0, 800) # we limit the x axis to 800 in order to avoid small pictures
# this is also allowed because the values of x-axis stop before
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
sns.displot(df_MCF7_unf_summary2.iloc[2], bins=600, color="skyblue")
plt.xlim(0, 800) # we limit the x axis to 800 in order to avoid small pictures
# this is also allowed because the values of x-axis stop before
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
#75% quantile
sns.displot(df_MCF7_unf_summary2.iloc[6], bins=600, color="purple")
plt.xlim(0, 800) # we limit the x axis to 800 in order to avoid small pictures
# this is also allowed because the values of x-axis stop before
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Also from this persepctive we can see that most genes have not value higher then zero. We can avoid to represent again the 25% and 50% quantiles
¶
1.1.8) CHECK OF NORMALIZATION OF DATA USING SKEWENESS AND KURTOSIS
Now we analyze the skewness and Kurtosis for each single cell to verify if it is normalized or not ( since most of these values are not near 0, we can deudce that most cells have a different distribution from the normal one).
from scipy.stats import kurtosis, skew
cnames = list(df_MCF7_unf.columns)
colN = np.shape(df_MCF7_unf)[1]
colN
df_skew_cells = []
for i in range(colN) :
v_df = df_MCF7_unf[cnames[i]]
df_skew_cells += [skew(v_df)]
df_skew_cells
sns.histplot(df_skew_cells,bins=100, color='blue', kde=True)
plt.xlabel('Skewness of single cells expression profiles - original df')
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
df_kurt_cells = []
for i in range(colN) :
v_df = df_MCF7_unf[cnames[i]]
df_kurt_cells += [kurtosis(v_df)]
df_kurt_cells
sns.histplot(df_kurt_cells,bins=100, color='green', kde=True)
plt.xlabel('Kurtosis of single cells expression profiles - original df')
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
# Now we compute the difference between the two distributions and our distributions created randomly
xnorm = np.random.normal(0, 2, 10000000) # create random values based on a normal distribution
xnorm
print( "Excess kurtosis of normal distribution: ", kurtosis(xnorm) )
print( "Skewness of normal distribution: ", skew(xnorm) )
Excess kurtosis of normal distribution: 0.000966120288296235 Skewness of normal distribution: -0.0004663586529028709
¶
1.1.9) DATA TRANSORMATIONS USING LOG 2
We now try to transform the data, computing the log base 2 across all the dataset in order to see if this transformation can be a good normalization
# First we add 1 to all the dataset to avoid log(0) and then we compute the log of the dataset
df_MCF7_unf_log = (df_MCF7_unf + 1).apply(np.log2)
df_MCF7_unf_log
| "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "WASH7P" | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 |
| "MIR6859-1" | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| "WASH9P" | 1.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 1.00000 | 3.459432 | 1.000000 | 0.000000 | 0.000000 | ... | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 2.321928 | 2.584963 |
| "OR4F29" | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 |
| "MTND1P23" | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| "MT-TE" | 2.321928 | 0.0 | 0.000000 | 0.000000 | 2.000000 | 2.00000 | 0.000000 | 0.000000 | 3.906891 | 1.000000 | ... | 0.000000 | 2.321928 | 3.700440 | 2.321928 | 0.000000 | 1.000000 | 2.807355 | 0.000000 | 3.000000 | 2.321928 |
| "MT-CYB" | 8.082149 | 1.0 | 6.266787 | 6.066089 | 9.507795 | 11.40833 | 13.187197 | 11.667999 | 12.956739 | 4.954196 | ... | 7.906891 | 11.890264 | 13.639567 | 11.144658 | 10.418907 | 9.157347 | 11.764042 | 8.451211 | 11.093418 | 10.167418 |
| "MT-TT" | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 1.00000 | 1.000000 | 1.000000 | 2.321928 | 0.000000 | ... | 0.000000 | 3.000000 | 2.321928 | 1.584963 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 1.584963 | 1.584963 |
| "MT-TP" | 2.584963 | 0.0 | 0.000000 | 1.000000 | 0.000000 | 1.00000 | 1.000000 | 2.321928 | 1.584963 | 0.000000 | ... | 0.000000 | 3.906891 | 5.832890 | 3.584963 | 1.584963 | 0.000000 | 3.000000 | 1.584963 | 4.857981 | 3.584963 |
| "MAFIP" | 3.169925 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 1.584963 | 1.000000 | 0.000000 | 2.807355 | 0.000000 | 1.000000 | 2.321928 |
22934 rows × 383 columns
# We now apply again the method .describe() to see how information as mean/std is changed
df_MCF7_unf_log_summary = df_MCF7_unf_log.describe()
df_MCF7_unf_log_summary
| "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | ... | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 |
| mean | 1.889979 | 0.009665 | 1.732075 | 0.408770 | 1.563776 | 2.174915 | 2.539393 | 2.600671 | 2.502254 | 0.627249 | ... | 1.659217 | 2.371224 | 0.511852 | 1.972339 | 1.744485 | 1.624858 | 2.145188 | 2.221317 | 2.368192 | 2.298742 |
| std | 2.743667 | 0.115893 | 3.060936 | 0.932712 | 2.158735 | 2.936550 | 3.166716 | 3.027012 | 3.107430 | 1.184292 | ... | 2.207388 | 2.864281 | 1.880059 | 2.656387 | 2.428364 | 2.241559 | 2.939852 | 2.998384 | 3.098435 | 2.993186 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 4.169925 | 0.000000 | 2.584963 | 0.000000 | 3.000000 | 4.584963 | 5.321928 | 5.169925 | 5.285402 | 1.000000 | ... | 3.321928 | 4.954196 | 0.000000 | 4.169925 | 3.700440 | 3.321928 | 4.807355 | 4.954196 | 5.285402 | 5.087463 |
| max | 15.512524 | 3.906891 | 16.324181 | 8.179909 | 13.369461 | 15.515977 | 14.850138 | 15.637446 | 15.145176 | 10.738092 | ... | 14.119671 | 14.511506 | 16.322509 | 14.850431 | 13.568669 | 14.235266 | 14.774272 | 15.313060 | 15.497540 | 16.069932 |
8 rows × 383 columns
We plot again the histograms of mean, standard deviation and third quantile. We notice that we have smaller intervals of values.
# we now plot more or less some charts (at least mean, std and 75% quartile)
sns.displot(data = df_MCF7_unf_log_summary.iloc[1], bins=50, color="green")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
sns.displot(data = df_MCF7_unf_log_summary.iloc[2], bins=50, color="skyblue")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
sns.displot(data = df_MCF7_unf_log_summary.iloc[6], bins=50, color="red")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Finally we plot again the skeweness and the kurtosis to understand if the transformed data are now normalized.
# We apply again skeweness and kurtosis to the log transformed dataset
from scipy.stats import kurtosis, skew
cnames = list(df_MCF7_unf_log.columns)
colN = np.shape(df_MCF7_unf_log)[1]
colN
df_skew_cells = []
for i in range(colN) :
v_df = df_MCF7_unf_log[cnames[i]]
df_skew_cells += [skew(v_df)]
# df_skew_cells += [df[cnames[i]].skew()]
df_skew_cells
sns.histplot(df_skew_cells,bins=100, color='blue', kde=True)
plt.xlabel('Skewness of single cells expression profiles - original df')
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
df_kurt_cells = []
for i in range(colN) :
v_df = df_MCF7_unf_log[cnames[i]]
df_kurt_cells += [kurtosis(v_df)]
# df_kurt_cells += [df[cnames[i]].kurt()]
df_kurt_cells
sns.histplot(df_kurt_cells,bins=100, color='green', kde=True)
plt.xlabel('Kurtosis of single cells expression profiles - original df')
plt.show()
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[357], line 2 1 df_kurt_cells = [] ----> 2 for i in range(colN) : 3 v_df = df_MCF7_unf_log[cnames[i]] 4 df_kurt_cells += [kurtosis(v_df)] NameError: name 'colN' is not defined
As final observation we can notice that most data has value zero in both skeweness and kurtosis telling us that there is no asymmetry and the data is more normalized.
¶
1.1.10) Study of PCA
¶
1.2) HCC1806
¶
1.2.1) READING THE META-DATASET USEFUL SINCE CONTAINS SOME INSIGHTS
We start downloading and opening the meta_data file of HCC1806
df_HCC1806_unf_meta = pd.read_csv("SmartSeq/HCC1806_SmartS_MetaData.tsv",delimiter="\t",engine='python',index_col=0)
print("Dataframe dimensions:", np.shape(df_HCC1806_unf_meta))
print("First column: ", df_HCC1806_unf_meta.iloc[ : , 0])
Dataframe dimensions: (243, 8)
First column: Filename
output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam HCC1806
output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam HCC1806
output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam HCC1806
output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam HCC1806
output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam HCC1806
...
output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam HCC1806
output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam HCC1806
output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam HCC1806
output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam HCC1806
output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam HCC1806
Name: Cell Line, Length: 243, dtype: object
df_HCC1806_unf_meta.shape
(243, 8)
As we can see the file has a shape of 243 rows and 8 columns and is made in the following way: the rows represent the cells while the columns give us information about the experiment, e.g. we have the cell line, the plate, the position of the cell, the condition (hypoxia or Normoxia), and other informations as the comment or tag.
Moreover the 243 cells tell us that in the main dataset we will have 243 cells (in that case as columns)
df_HCC1806_unf_meta.head(243)
| Cell Line | PCR Plate | Pos | Condition | Hours | Cell name | PreprocessingTag | ProcessingComments | |
|---|---|---|---|---|---|---|---|---|
| Filename | ||||||||
| output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam | HCC1806 | 1 | A10 | Normo | 24 | S123 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam | HCC1806 | 1 | A12 | Normo | 24 | S26 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam | HCC1806 | 1 | A1 | Hypo | 24 | S97 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam | HCC1806 | 1 | A2 | Hypo | 24 | S104 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam | HCC1806 | 1 | A3 | Hypo | 24 | S4 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam | HCC1806 | 4 | H10 | Normo | 24 | S210 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam | HCC1806 | 4 | H11 | Normo | 24 | S214 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam | HCC1806 | 4 | H2 | Hypo | 24 | S199 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam | HCC1806 | 4 | H7 | Normo | 24 | S205 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
| output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam | HCC1806 | 4 | H9 | Normo | 24 | S236 | Aligned.sortedByCoord.out.bam | STAR,FeatureCounts |
243 rows × 8 columns
df_HCC1806_unf_meta.describe()
| PCR Plate | Hours | |
|---|---|---|
| count | 243.000000 | 243.0 |
| mean | 2.411523 | 24.0 |
| std | 1.081107 | 0.0 |
| min | 1.000000 | 24.0 |
| 25% | 1.000000 | 24.0 |
| 50% | 2.000000 | 24.0 |
| 75% | 3.000000 | 24.0 |
| max | 4.000000 | 24.0 |
We see that every cell stays in a defined PCR plate for 24 hours (mean is 24, std is 0)
df_HCC1806_unf_meta["Condition"].value_counts().plot(kind='bar')
number_of_cells=df_HCC1806_unf_meta["Condition"].value_counts()
print(number_of_cells)
Condition Hypo 126 Normo 117 Name: count, dtype: int64
Hypoxic cells are slightly more than Normoxyc ones (51.85% of the total meta dataset)
df_HCC1806_unf_meta.hist(column='PCR Plate', bins=30)
number_of_plates=df_HCC1806_unf_meta["PCR Plate"].value_counts()
print(number_of_plates)
PCR Plate 3 68 1 64 2 63 4 48 Name: count, dtype: int64
df_HCC1806_unf_meta.hist(column='PCR Plate', by='Condition', bins=30)
# Filter the DataFrame for "Hypo" and "Normo" elements
hypo_elements = df_HCC1806_unf_meta[df_HCC1806_unf_meta['Condition'] == 'Hypo']
normo_elements = df_HCC1806_unf_meta[df_HCC1806_unf_meta['Condition'] == 'Normo']
# Count the number of "Hypo" and "Normo" elements in each PCR Plate
hypo_counts = hypo_elements['PCR Plate'].value_counts()
normo_counts = normo_elements['PCR Plate'].value_counts()
# Print the counts for each PCR Plate
for plate in sorted(set(df_HCC1806_unf_meta['PCR Plate'])):
hypo_count = hypo_counts.get(plate, 0)
normo_count = normo_counts.get(plate, 0)
print(f"PCR Plate {plate}: Hypo = {hypo_count}, Normo = {normo_count}")
PCR Plate 1: Hypo = 32, Normo = 32 PCR Plate 2: Hypo = 34, Normo = 29 PCR Plate 3: Hypo = 39, Normo = 29 PCR Plate 4: Hypo = 21, Normo = 27
On plate 1 Hypoxic and Normoxic cells are equally distributed
On plate 2 there are a bit more Hypoxic cells (54%)
Also on plate 3 there are more Hypoxic cells (57.4%)
Instead, on plate 4 there are slightly more Normoxic cells (56.25%)
¶
1.2.2) OPENING THE MAIN DATASET: UNFILTERED HCC1806 DATASET PRODUCED USING THE SMART SEQUENCING TECHNOLOGY
We start now opening the HCC1806 SmartSeq unfiltered dataset since we are interested in analyzing it.
df_HCC1806_unf = pd.read_csv("SmartSeq/HCC1806_SmartS_Unfiltered_Data.txt",delimiter="\ ",engine='python',index_col=0)
print("Dataframe dimensions:", np.shape(df_HCC1806_unf))
print("First column: ", df_HCC1806_unf.iloc[ : , :])
Dataframe dimensions: (23396, 243)
First column: "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 2
... ...
"MT-TE" 22
"MT-CYB" 4208
"MT-TT" 26
"MT-TP" 66
"MAFIP" 0
"output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 43
"MT-CYB" 6491
"MT-TT" 62
"MT-TP" 71
"MAFIP" 4
"output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 0
"MT-CYB" 25
"MT-TT" 0
"MT-TP" 1
"MAFIP" 0
"output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 0
"MT-CYB" 4819
"MT-TT" 11
"MT-TP" 3
"MAFIP" 7
"output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 0
"MT-CYB" 310
"MT-TT" 4
"MT-TP" 9
"MAFIP" 0
"output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 1
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 3
"MT-CYB" 695
"MT-TT" 0
"MT-TP" 14
"MAFIP" 9
"output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 1
... ...
"MT-TE" 47
"MT-CYB" 2885
"MT-TT" 41
"MT-TP" 91
"MAFIP" 0
"output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 4
"MT-CYB" 1552
"MT-TT" 9
"MT-TP" 22
"MAFIP" 4
"output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 1
"OR4F29" 0
... ...
"MT-TE" 2
"MT-CYB" 366
"MT-TT" 2
"MT-TP" 3
"MAFIP" 2
"output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 8
"MT-CYB" 1829
"MT-TT" 8
"MT-TP" 30
"MAFIP" 0
... \
"WASH7P" ...
"CICP27" ...
"DDX11L17" ...
"WASH9P" ...
"OR4F29" ...
... ...
"MT-TE" ...
"MT-CYB" ...
"MT-TT" ...
"MT-TP" ...
"MAFIP" ...
"output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 24
"MT-CYB" 1119
"MT-TT" 48
"MT-TP" 119
"MAFIP" 2
"output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 15
"MT-CYB" 1429
"MT-TT" 31
"MT-TP" 52
"MAFIP" 0
"output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 15
"MT-CYB" 808
"MT-TT" 3
"MT-TP" 11
"MAFIP" 2
"output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 4
"MT-CYB" 999
"MT-TT" 8
"MT-TP" 22
"MAFIP" 1
"output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 4
"MT-CYB" 916
"MT-TT" 5
"MT-TP" 15
"MAFIP" 1
"output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 1
"OR4F29" 0
... ...
"MT-TE" 26
"MT-CYB" 3719
"MT-TT" 42
"MT-TP" 48
"MAFIP" 3
"output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 1
"MT-CYB" 984
"MT-TT" 1
"MT-TP" 18
"MAFIP" 0
"output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 1
"OR4F29" 0
... ...
"MT-TE" 4
"MT-CYB" 2256
"MT-TT" 15
"MT-TP" 36
"MAFIP" 2
"output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" \
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 4
"MT-CYB" 981
"MT-TT" 6
"MT-TP" 8
"MAFIP" 1
"output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"
"WASH7P" 0
"CICP27" 0
"DDX11L17" 0
"WASH9P" 0
"OR4F29" 0
... ...
"MT-TE" 20
"MT-CYB" 2039
"MT-TT" 34
"MT-TP" 79
"MAFIP" 5
[23396 rows x 243 columns]
df_HCC1806_unf.head(243)
| "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "WASH7P" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "CICP27" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "DDX11L17" | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "WASH9P" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| "OR4F29" | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| "RPL12P14" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "UQCRHL" | 14 | 12 | 2 | 16 | 3 | 1 | 15 | 17 | 3 | 13 | ... | 4 | 11 | 7 | 2 | 3 | 4 | 0 | 11 | 1 | 9 |
| "SPEN" | 75 | 519 | 11 | 0 | 23 | 0 | 322 | 21 | 137 | 5 | ... | 35 | 123 | 13 | 39 | 46 | 15 | 11 | 81 | 35 | 34 |
| "ZBTB17" | 60 | 0 | 12 | 0 | 35 | 0 | 198 | 0 | 0 | 0 | ... | 73 | 0 | 0 | 0 | 18 | 211 | 24 | 69 | 20 | 5 |
| "CLCNKA" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
243 rows × 243 columns
df_HCC1806_unf.shape # this gives us the shape of the dataframe
(23396, 243)
The HCC1806 SmartSeq unfiltered dataset is made of 23396 rows representing specific genes and 243 columns representing the cells (already available in the meta dataset). Therefore each entry tells us how much of every gene is expressed in every cells. Moreover, each cell has in its name written the state in which it was under (normoxia / hypoxia), and such information was given as a separate column in the meta dataset.
#We return the indexes of the dataframe, which are the gene symbols
gene_symbls = df_HCC1806_unf.index
print("Dataframe indexes: ", gene_symbls)
Dataframe indexes: Index(['"WASH7P"', '"CICP27"', '"DDX11L17"', '"WASH9P"', '"OR4F29"',
'"MTND1P23"', '"MTND2P28"', '"MTCO1P12"', '"MTCO2P12"', '"MTATP8P1"',
...
'"MT-TH"', '"MT-TS2"', '"MT-TL2"', '"MT-ND5"', '"MT-ND6"', '"MT-TE"',
'"MT-CYB"', '"MT-TT"', '"MT-TP"', '"MAFIP"'],
dtype='object', length=23396)
¶
1.2.3) CHECKING MISSING AND NON VALID VALUES IN OUR DATASET
We now perform some operations to see if there are non numeric or non valid types in our dataset.
# We now print all data types of our dataframe
print("Data types: ", df_HCC1806_unf.dtypes)
Data types: "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" int64
"output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" int64
"output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" int64
"output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" int64
"output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" int64
...
"output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" int64
"output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" int64
"output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" int64
"output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" int64
"output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" int64
Length: 243, dtype: object
# We create a dataframe from the original one with just the non-numeric columns to see if there are indeed non numeric values
non_numeric_df_HCC1806_unf = df_HCC1806_unf.select_dtypes(exclude=['int64', 'float64'])
# Print the data types of the non-numeric columns
print("Non-numeric data types: ", non_numeric_df_HCC1806_unf.dtypes)
Non-numeric data types: Series([], dtype: object)
Apart of numeric values, there are not other types of values in our dataset.
# We now perform an additional step to explicitly check for NaN values in the dataframe
nan_df_HCC1806 = df_HCC1806_unf.isna()
# Print the columns that contain NaN values
print("Columns with NaN values: ", nan_df_HCC1806.any())
Columns with NaN values: "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" False
"output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" False
"output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" False
"output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" False
"output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" False
...
"output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" False
"output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" False
"output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" False
"output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" False
"output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" False
Length: 243, dtype: bool
print(df_HCC1806_unf.isnull().sum()) # we return the sum of null values in our dataset
"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" 0
"output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" 0
"output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" 0
"output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" 0
"output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" 0
..
"output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" 0
"output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" 0
"output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" 0
"output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" 0
"output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" 0
Length: 243, dtype: int64
We see that the sum of all null value in our dataset for each cell is zero, therefore we don't need to do any additional change to our dataset.
# We now try to drop missing values to see if indeed there are any of them
df_HCC1806_unf = df_HCC1806_unf.dropna()
df_HCC1806_unf.shape
# we see that the shape of the dataframe is the same, meaning that there are no missing values in the dataset
(23396, 243)
# No duplicates
df_HCC1806_unf_without_duplicates = df_HCC1806_unf.drop_duplicates()
df_HCC1806_unf_without_duplicates.shape
(23342, 243)
Checking for duplicates we get a new dataframe with a shape of (23343, 243) that is different to the one of the original dataset (23396, 243). This seems to suggest that there are indeed duplicates in our dataset
¶
1.2.4) COMPUTATION AND VISUALIZATION OF MEAN, STD, QUANTILES, MAX AND MIN
We now look at some information in our unfiltered HCC1806 dataset. We start using the describe method which returns for each cells the mean, std, the 25,50,75 quantiles based on the genes
# We start applying descriptive statistics to our dataset using the method .describe()
df_HCC1806_unf_summary1 = df_HCC1806_unf.describe()
df_HCC1806_unf_summary1
| "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | ... | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 |
| mean | 99.565695 | 207.678278 | 9.694734 | 150.689007 | 35.700504 | 47.088434 | 152.799453 | 135.869422 | 38.363908 | 45.512139 | ... | 76.361771 | 105.566593 | 54.026116 | 29.763806 | 28.905411 | 104.740725 | 35.181569 | 108.197940 | 37.279962 | 76.303855 |
| std | 529.532443 | 981.107905 | 65.546050 | 976.936548 | 205.885369 | 545.367706 | 864.974182 | 870.729740 | 265.062493 | 366.704721 | ... | 346.659348 | 536.881574 | 344.068304 | 186.721266 | 135.474736 | 444.773045 | 170.872090 | 589.082268 | 181.398951 | 369.090274 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| 75% | 51.000000 | 125.000000 | 5.000000 | 40.000000 | 22.000000 | 17.000000 | 81.000000 | 76.000000 | 22.000000 | 18.000000 | ... | 56.000000 | 67.000000 | 29.000000 | 18.000000 | 19.000000 | 76.000000 | 24.000000 | 68.000000 | 22.000000 | 44.000000 |
| max | 35477.000000 | 69068.000000 | 6351.000000 | 70206.000000 | 17326.000000 | 47442.000000 | 43081.000000 | 62813.000000 | 30240.000000 | 35450.000000 | ... | 19629.000000 | 30987.000000 | 21894.000000 | 13457.000000 | 11488.000000 | 33462.000000 | 15403.000000 | 34478.000000 | 10921.000000 | 28532.000000 |
8 rows × 243 columns
As for the MCF7, also here we can see some interesting facts about our dataset:
- there are many cells that have the mean smaller then the maximum value (e.g in the first cell we can see the mean equal to almost 100 and the maximum value be 35477) and this seems to tell us that a lot of genes in our cells are not expressed while other are extremely characteristic.
- there are also some cells which have mean almost equal zero and this tells us that probably they don't have so much influence in the entire scenario (maybe because these cells will die or for other factors which are kind of domain questions)
Now we go deep into our analysis plotting again (as we did in the other dataset) the distribution of the mean in the cells with an histogram. The plot shows us that there are two main peaks: one near zero, so there are a lot of cells with a mean of almost zero and therefore very few genes expressed, and another peak between 30 and 40.
# Now let's try to represent through histograms the mean of the cells and how many times the different values appear
sns.displot(df_HCC1806_unf_summary1.iloc[1], bins=70, color="green")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
We try to achieve the same plotting the distribution of the standard deviation in the cells with an histogram. We can see again two peaks, the first again around 0 and the second between 200 and 250. The standard deviation is not constant over the cells and there is also some dispersion in the dataset.
sns.displot(df_HCC1806_unf_summary1.iloc[2], bins=70, color="skyblue")
plt.show()
# the cells with high std are the ones with high number of genes expressed
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Now we plot histograms of 25, 50, 75-percentile of the distribution of all the cells and we notice the following things:
- in the 25-percentile we have all cells with zero value (suggesting that 25% of genes in each cell has value equal zero)
- in the 50-percentule we have most cells with zero value (sugggesting that 50% of genes in each cell has value equal zero)
- in the 75% we have an interesting scenario since it seems that in average this percentile is smaller then the mean, suggesting that most cells are zero values with some having very large values.
# See what happens with 75% quartile
sns.displot(data = df_HCC1806_unf_summary1.iloc[6], bins=50, color="red")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
# See what happens with 25% quartile
sns.displot(data = df_HCC1806_unf_summary1.iloc[4], bins=50, color="purple")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
# See what happens with 50% quartile
sns.displot(data = df_HCC1806_unf_summary1.iloc[5], bins=50, color="purple")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Finally, we represent again the violin plot of all the cells (it is a little bit chaotic since there are many samples) to see how the distribution of each cell is concentrated.
Again Some of the samples have a distribution which is incredibly concentrated near zero (they are the ones with very short lines) while there are others represented by long line which have a wide range of gene expression levels, from very low to very high (in particular the length of these lines shows how far the most extreme values are from the median).
plt.figure(figsize=(16,4))
plot=sns.violinplot(data= df_HCC1806_unf,palette="Set3",cut=0)
plt.show()
¶
1.2.5) CHECK OF OUTLIERS
We now try to find the outliers, eliminate them from the dataset and see what changes respect to the original situation
# Computation of the inter quantile range.
Q1 = df_HCC1806_unf.quantile(0.25)
Q3 = df_HCC1806_unf.quantile(0.75)
IQR = Q3 - Q1
print(IQR)
"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" 51.0
"output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" 125.0
"output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" 5.0
"output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" 40.0
"output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" 22.0
...
"output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" 76.0
"output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" 24.0
"output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" 68.0
"output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" 22.0
"output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" 44.0
Length: 243, dtype: float64
df_HCC1806_noOut = df_HCC1806_unf[~((df_HCC1806_unf < (Q1 - 1.5 * IQR)) |(df_HCC1806_unf > (Q3 + 1.5 * IQR))).any(axis=1)]
df_HCC1806_noOut.shape
#df_noOut.head(3)
# important: we get a shaper of (10815,243), it is quite important to note that we have removed a lot of data, since before we had (23396, 243)
# They don't look good. We have just zeros now. Simply removing outliers would not work here, as many genes/features would simply be 0 if we excluded the outliers.
# So we need to proceed differently, as the outliers seem to be the values carrying the information in this case (this has been told by the professor)
(10815, 243)
df_HCC1806_noOut.shape
(10815, 243)
Also in this case we can notice that after having removed the outliers we have remained with a smaller dataset (precisely the shape is just of (10815, 243)) and most of them are zero. The reason is because actually the genes carry most of information were the ones in the last quartile.
Therefore we now come back to the original situation and investigate the sparsity of the data.
¶
1.2.6) SPARSITY OF DATA
We have seen thorugh previosly charts and outliers checks that most genes which carry values are out of the 75% quartile, therefore removing the outliers is not worth. Moreover this is explained by the fact that most of data is sparse.
We now represent the dataframe as a simple matrix and look at how many zero we have. Then we compute the percentage of the sparsity on our dataset (we basically subtract to one the number_of_non_zero_genes/total_number of genes)
# We proceed in the following way. We start representing the dataframe as a simple matrix to see how many zero we have
# then we compute the percentage of the sparsity on our dataset doing 1 - number_of_non_zero_genes / total_number of genes
df_HCC1806_unf_Matrix = np.array(df_HCC1806_unf)
print("Matrix useful for the sparsity check: \n", df_HCC1806_unf_Matrix)
Matrix useful for the sparsity check: [[ 0 0 0 ... 0 0 0] [ 0 0 0 ... 0 0 0] [ 0 0 0 ... 0 0 0] ... [26 62 0 ... 15 6 34] [66 71 1 ... 36 8 79] [ 0 4 0 ... 2 1 5]]
Sparsity_matrix = 1 - count_nonzero(df_HCC1806_unf_Matrix)/ df_HCC1806_unf_Matrix.size
Sparsity_matrix
0.558456230779135
Again (as in the case of MCF7) also the dataframe of HCC1806 is highly sparse, infact the percentage of sparsity is more then 55% of the total dataframe.
¶
1.2.7) DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS
We now repeat the same steps (representation of means, std, etc) using the dataset transposed in order to see which genes are highly expressed with respect to the others
df_HCC1806_unf_transposed = df_HCC1806_unf.transpose() # we transpose the dataset in order to get cells
# as rows and genes as columns
df_HCC1806_unf_transposed
| "WASH7P" | "CICP27" | "DDX11L17" | "WASH9P" | "OR4F29" | "MTND1P23" | "MTND2P28" | "MTCO1P12" | "MTCO2P12" | "MTATP8P1" | ... | "MT-TH" | "MT-TS2" | "MT-TL2" | "MT-ND5" | "MT-ND6" | "MT-TE" | "MT-CYB" | "MT-TT" | "MT-TP" | "MAFIP" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 2 | 250 | 54 | 1740 | 6 | 1 | ... | 17 | 5 | 15 | 3852 | 900 | 22 | 4208 | 26 | 66 | 0 |
| "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 0 | 424 | 100 | 2340 | 5 | 1 | ... | 49 | 29 | 36 | 7457 | 1439 | 43 | 6491 | 62 | 71 | 4 |
| "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 22 | 0 | 0 | ... | 0 | 0 | 0 | 93 | 12 | 0 | 25 | 0 | 1 | 0 |
| "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 0 | 63 | 16 | 1020 | 3 | 0 | ... | 43 | 17 | 8 | 1479 | 234 | 0 | 4819 | 11 | 3 | 7 |
| "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 0 | 27 | 3 | 303 | 0 | 0 | ... | 0 | 0 | 3 | 303 | 33 | 0 | 310 | 4 | 9 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 1 | 0 | 146 | 47 | 961 | 1 | 0 | ... | 25 | 17 | 11 | 2926 | 423 | 26 | 3719 | 42 | 48 | 3 |
| "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 0 | 37 | 6 | 372 | 0 | 0 | ... | 2 | 1 | 3 | 688 | 114 | 1 | 984 | 1 | 18 | 0 |
| "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 1 | 0 | 83 | 30 | 1128 | 4 | 0 | ... | 9 | 6 | 23 | 2999 | 486 | 4 | 2256 | 15 | 36 | 2 |
| "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 0 | 47 | 7 | 258 | 1 | 0 | ... | 2 | 3 | 3 | 611 | 75 | 4 | 981 | 6 | 8 | 1 |
| "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" | 0 | 0 | 0 | 0 | 0 | 249 | 38 | 980 | 0 | 0 | ... | 14 | 3 | 15 | 2460 | 513 | 20 | 2039 | 34 | 79 | 5 |
243 rows × 23396 columns
df_HCC1806_unf_summary2 = df_HCC1806_unf_transposed.describe()
df_HCC1806_unf_summary2
| "WASH7P" | "CICP27" | "DDX11L17" | "WASH9P" | "OR4F29" | "MTND1P23" | "MTND2P28" | "MTCO1P12" | "MTCO2P12" | "MTATP8P1" | ... | "MT-TH" | "MT-TS2" | "MT-TL2" | "MT-ND5" | "MT-ND6" | "MT-TE" | "MT-CYB" | "MT-TT" | "MT-TP" | "MAFIP" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | ... | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 |
| mean | 0.045267 | 0.119342 | 0.469136 | 0.255144 | 0.127572 | 117.930041 | 28.427984 | 904.308642 | 1.403292 | 0.378601 | ... | 10.358025 | 5.930041 | 13.493827 | 2290.213992 | 386.901235 | 18.246914 | 2163.588477 | 20.613169 | 46.444444 | 3.897119 |
| std | 0.318195 | 0.594531 | 1.455282 | 0.818639 | 0.440910 | 103.038022 | 26.062662 | 654.520308 | 1.735003 | 0.747361 | ... | 10.910199 | 7.040559 | 11.803967 | 1726.423259 | 309.276105 | 54.076514 | 1730.393947 | 22.224590 | 47.684223 | 4.736193 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 46.500000 | 8.000000 | 390.500000 | 0.000000 | 0.000000 | ... | 2.000000 | 1.000000 | 4.000000 | 918.000000 | 138.500000 | 4.000000 | 947.500000 | 5.000000 | 14.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 94.000000 | 22.000000 | 790.000000 | 1.000000 | 0.000000 | ... | 7.000000 | 4.000000 | 10.000000 | 1848.000000 | 320.000000 | 11.000000 | 1774.000000 | 14.000000 | 38.000000 | 2.000000 |
| 75% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 157.000000 | 42.000000 | 1208.000000 | 2.000000 | 1.000000 | ... | 14.000000 | 7.500000 | 20.000000 | 3172.000000 | 528.000000 | 20.000000 | 2927.000000 | 30.500000 | 64.500000 | 6.000000 |
| max | 3.000000 | 5.000000 | 12.000000 | 6.000000 | 4.000000 | 694.000000 | 120.000000 | 3569.000000 | 12.000000 | 4.000000 | ... | 52.000000 | 43.000000 | 57.000000 | 8972.000000 | 1439.000000 | 804.000000 | 11383.000000 | 154.000000 | 409.000000 | 24.000000 |
8 rows × 23396 columns
Plotting the distributionn of the mean in the genes, the std, and the 75-percentile we can notice that most of genes are not present in mst of the cells.
# We now plot more or less the same graph of before to see the expression of individual genes across cells
sns.displot(df_HCC1806_unf_summary2.iloc[1], bins=600, color="green")
plt.xlim(0, 800) # we limit the x axis to 800 in order to avoid small pictures
# this is also allowed because the values of x-axis stop before
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
sns.displot(df_HCC1806_unf_summary2.iloc[2], bins=600, color="skyblue")
plt.xlim(0, 800) # we limit the x axis to 800 in order to avoid small pictures
# this is also allowed because the values of x-axis stop before
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
#75% quantile
sns.displot(df_HCC1806_unf_summary2.iloc[6], bins=600, color="purple")
plt.xlim(0, 800) # we limit the x axis to 800 in order to avoid small pictures
# this is also allowed because the values of x-axis stop before
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Also from this persepctive we can see that most genes have not value higher then zero. We can avoid to represent again the 25% and 50% quantiles
¶
1.2.8) CHECK OF NORMALIZATION OF DATA USING SKEWENESS AND KURTOSIS
Now we analyze the skewness and Kurtosis for each single cell to verify if it is normalized or not ( since most of these values are not near 0, we can deudce that most cells have a different distribution from the normal one).
from scipy.stats import kurtosis, skew
cnames = list(df_HCC1806_unf.columns)
colN = np.shape(df_HCC1806_unf)[1]
colN
df_skew_cells = []
for i in range(colN) :
v_df = df_HCC1806_unf[cnames[i]]
df_skew_cells += [skew(v_df)]
df_skew_cells
sns.histplot(df_skew_cells,bins=100, color='blue', kde=True)
plt.xlabel('Skewness of single cells expression profiles - original df')
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
df_kurt_cells = []
for i in range(colN) :
v_df = df_HCC1806_unf[cnames[i]]
df_kurt_cells += [kurtosis(v_df)]
df_kurt_cells
sns.histplot(df_kurt_cells,bins=100, color='green', kde=True)
plt.xlabel('Kurtosis of single cells expression profiles - original df')
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
# Now we compute the difference between the two distributions and our distributions
xnorm = np.random.normal(0, 2, 10000000) # create random values based on a normal distribution
xnorm
print( "Excess kurtosis of normal distribution: ", kurtosis(xnorm) )
print( "Skewness of normal distribution: ", skew(xnorm) )
Excess kurtosis of normal distribution: -0.0010484252979767739 Skewness of normal distribution: -0.001307851392970603
sss
¶
1.2.9) DATA TRANSORMATIONS USING LOG 2
We now try to transform the data, computing the log base 2 across all the dataset in order to see if this transformation can be a good normalization
# First we add 1 to all the dataset to avoid log(0) and then we compute the log of the dataset
df_HCC1806_unf_log = (df_HCC1806_unf + 1).apply(np.log2)
df_HCC1806_unf_log
| "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "WASH7P" | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| "CICP27" | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| "DDX11L17" | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| "WASH9P" | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 |
| "OR4F29" | 1.584963 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| "MT-TE" | 4.523562 | 5.459432 | 0.00000 | 0.000000 | 0.000000 | 2.000000 | 5.584963 | 2.321928 | 1.584963 | 3.169925 | ... | 4.643856 | 4.000000 | 4.000000 | 2.321928 | 2.321928 | 4.754888 | 1.000000 | 2.321928 | 2.321928 | 4.392317 |
| "MT-CYB" | 12.039262 | 12.664447 | 4.70044 | 12.234817 | 8.280771 | 9.442943 | 11.494856 | 10.600842 | 8.519636 | 10.837628 | ... | 10.129283 | 10.481799 | 9.659996 | 9.965784 | 9.840778 | 11.861087 | 9.943980 | 11.140191 | 9.939579 | 10.994353 |
| "MT-TT" | 4.754888 | 5.977280 | 0.00000 | 3.584963 | 2.321928 | 0.000000 | 5.392317 | 3.321928 | 1.584963 | 3.169925 | ... | 5.614710 | 5.000000 | 2.000000 | 3.169925 | 2.584963 | 5.426265 | 1.000000 | 4.000000 | 2.807355 | 5.129283 |
| "MT-TP" | 6.066089 | 6.169925 | 1.00000 | 2.000000 | 3.321928 | 3.906891 | 6.523562 | 4.523562 | 2.000000 | 4.954196 | ... | 6.906891 | 5.727920 | 3.584963 | 4.523562 | 4.000000 | 5.614710 | 4.247928 | 5.209453 | 3.169925 | 6.321928 |
| "MAFIP" | 0.000000 | 2.321928 | 0.00000 | 3.000000 | 0.000000 | 3.321928 | 0.000000 | 2.321928 | 1.584963 | 0.000000 | ... | 1.584963 | 0.000000 | 1.584963 | 1.000000 | 1.000000 | 2.000000 | 0.000000 | 1.584963 | 1.000000 | 2.584963 |
23396 rows × 243 columns
# We now apply again the method .describe() to see how information as mean/std is changed
df_HCC1806_unf_log_summary = df_HCC1806_unf_log.describe()
df_HCC1806_unf_log_summary
| "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | ... | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 |
| mean | 2.665234 | 3.224613 | 1.366411 | 2.261582 | 2.109583 | 1.827055 | 2.635027 | 2.887775 | 2.028888 | 1.877170 | ... | 2.822969 | 2.797182 | 2.234019 | 2.030740 | 2.048194 | 3.118012 | 2.252949 | 2.805019 | 2.163035 | 2.764438 |
| std | 3.341225 | 3.853466 | 1.938462 | 3.620534 | 2.760889 | 2.810320 | 3.651689 | 3.546553 | 2.807174 | 2.831652 | ... | 3.245582 | 3.438419 | 2.969904 | 2.611961 | 2.648009 | 3.413622 | 2.758163 | 3.443798 | 2.781778 | 3.167457 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.584963 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| 75% | 5.700440 | 6.977280 | 2.584963 | 5.357552 | 4.523562 | 4.169925 | 6.357552 | 6.266787 | 4.523562 | 4.247928 | ... | 5.832890 | 6.087463 | 4.906891 | 4.247928 | 4.321928 | 6.266787 | 4.643856 | 6.108524 | 4.523562 | 5.491853 |
| max | 15.114637 | 16.075751 | 12.632995 | 16.099327 | 14.080734 | 15.533908 | 15.394798 | 15.938799 | 14.884218 | 15.113539 | ... | 14.260773 | 14.919422 | 14.418314 | 13.716176 | 13.487966 | 15.030279 | 13.911017 | 15.073430 | 13.414949 | 14.800344 |
8 rows × 243 columns
# we now plot more or less some charts (at least mean, std and 75% quartile)
sns.displot(data = df_HCC1806_unf_log_summary.iloc[1], bins=50, color="green")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
sns.displot(data = df_HCC1806_unf_log_summary.iloc[2], bins=50, color="skyblue")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
sns.displot(data = df_HCC1806_unf_log_summary.iloc[6], bins=50, color="red")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
What we notice is that we have less zero and smaller ranges compared to the original situation
# We apply again skeweness and kurtosis to the log transformed dataset
from scipy.stats import kurtosis, skew
cnames = list(df_HCC1806_unf_log.columns)
colN = np.shape(df_HCC1806_unf_log)[1]
colN
df_skew_cells = []
for i in range(colN) :
v_df = df_HCC1806_unf_log[cnames[i]]
df_skew_cells += [skew(v_df)]
df_skew_cells
sns.histplot(df_skew_cells,bins=100, color='blue', kde=True)
plt.xlabel('Skewness of single cells expression profiles - original df')
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
df_kurt_cells = []
for i in range(colN) :
v_df = df_HCC1806_unf_log[cnames[i]]
df_kurt_cells += [kurtosis(v_df)]
df_kurt_cells
sns.histplot(df_kurt_cells,bins=100, color='green', kde=True)
plt.xlabel('Kurtosis of single cells expression profiles - original df')
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
As final observation we can notice that most data has value zero in both skeweness and kurtosis telling us that there is no asymmetry and the data is more normalized.
¶
- Analysis on unfiltered/filtered Smartseq and Unsupervided/Supervised learning on SmartSeq
¶
2.1) MCF7
¶
2.1.0) Importing datasets
df_MCF7_unf = pd.read_csv("SmartSeq\MCF7_SmartS_Unfiltered_Data.txt",delimiter="\ ", engine='python',index_col=0)
print("UNFILTERED dimensions:", np.shape(df_MCF7_unf))
df_MCF7_unf_summary=df_MCF7_unf.describe()
df_MCF7_unf.describe()
UNFILTERED dimensions: (22934, 383)
| "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | ... | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 |
| mean | 40.817651 | 0.012253 | 86.442400 | 1.024636 | 14.531351 | 56.213613 | 75.397183 | 62.767725 | 67.396747 | 2.240734 | ... | 17.362562 | 42.080230 | 34.692422 | 32.735284 | 21.992718 | 17.439391 | 49.242784 | 61.545609 | 68.289352 | 62.851400 |
| std | 465.709940 | 0.207726 | 1036.572689 | 6.097362 | 123.800530 | 503.599145 | 430.471519 | 520.167576 | 459.689019 | 25.449630 | ... | 193.153757 | 256.775704 | 679.960908 | 300.291051 | 153.441647 | 198.179666 | 359.337479 | 540.847355 | 636.892085 | 785.670341 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 17.000000 | 0.000000 | 5.000000 | 0.000000 | 7.000000 | 23.000000 | 39.000000 | 35.000000 | 38.000000 | 1.000000 | ... | 9.000000 | 30.000000 | 0.000000 | 17.000000 | 12.000000 | 9.000000 | 27.000000 | 30.000000 | 38.000000 | 33.000000 |
| max | 46744.000000 | 14.000000 | 82047.000000 | 289.000000 | 10582.000000 | 46856.000000 | 29534.000000 | 50972.000000 | 36236.000000 | 1707.000000 | ... | 17800.000000 | 23355.000000 | 81952.000000 | 29540.000000 | 12149.000000 | 19285.000000 | 28021.000000 | 40708.000000 | 46261.000000 | 68790.000000 |
8 rows × 383 columns
mcf_filtered = pd.read_csv("SmartSeq\MCF7_SmartS_Filtered_Data.txt",delimiter="\ ", engine='python',index_col=0)
print("FILTERED dimensions:", np.shape(mcf_filtered))
mcf_filtered_summary=mcf_filtered.describe()
mcf_filtered.describe()
FILTERED dimensions: (18945, 313)
| "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B11_Hypo_S77_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B12_Hypo_S78_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B4_Norm_S52_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.4_H10_Hypo_S382_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | ... | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 | 18945.000000 |
| mean | 49.409290 | 104.620639 | 17.589707 | 68.045395 | 91.260333 | 75.979784 | 81.576194 | 85.303985 | 49.655529 | 16.382792 | ... | 54.711375 | 21.016785 | 50.920137 | 39.622486 | 26.620164 | 21.099023 | 59.585537 | 74.487305 | 82.655054 | 76.081499 |
| std | 511.986757 | 1139.662971 | 136.014975 | 553.362211 | 472.099720 | 571.441098 | 504.632248 | 911.153373 | 406.561440 | 160.981562 | ... | 633.970615 | 212.338278 | 281.722199 | 329.984580 | 168.460343 | 217.871697 | 394.584632 | 594.260858 | 699.898130 | 863.857880 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 4.000000 | 6.000000 | 4.000000 | 1.000000 | 3.000000 | 0.000000 | ... | 0.000000 | 1.000000 | 4.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 |
| 75% | 28.000000 | 27.000000 | 10.000000 | 36.000000 | 57.000000 | 52.000000 | 55.000000 | 48.000000 | 33.000000 | 9.000000 | ... | 28.000000 | 13.000000 | 42.000000 | 25.000000 | 17.000000 | 13.000000 | 42.000000 | 45.000000 | 55.000000 | 48.000000 |
| max | 46744.000000 | 82047.000000 | 10582.000000 | 46856.000000 | 29534.000000 | 50972.000000 | 36236.000000 | 56068.000000 | 24994.000000 | 13587.000000 | ... | 49147.000000 | 17800.000000 | 23355.000000 | 29540.000000 | 12149.000000 | 19285.000000 | 28021.000000 | 40708.000000 | 46261.000000 | 68790.000000 |
8 rows × 313 columns
mcf_removed_elements = df_MCF7_unf[~df_MCF7_unf.index.isin(mcf_filtered.index)]
columns_to_remove = mcf_filtered.columns.intersection(mcf_removed_elements.columns)
mcf_removed_elements = df_MCF7_unf.drop(columns=columns_to_remove)
mcf_removed_elements_summary=mcf_removed_elements.describe()
print("REMOVED DATA dimensions:", np.shape(mcf_removed_elements))
mcf_removed_elements.describe()
REMOVED DATA dimensions: (22934, 70)
| "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A8_Hypo_S26_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A9_Hypo_S27_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B10_Hypo_S76_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B1_Norm_S49_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B2_Norm_S50_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B3_Norm_S51_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B9_Hypo_S75_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.3_E10_Hypo_S232_Aligned.sortedByCoord.out.bam" | "output.STAR.3_E3_Norm_S207_Aligned.sortedByCoord.out.bam" | "output.STAR.3_E7_Hypo_S229_Aligned.sortedByCoord.out.bam" | "output.STAR.3_F11_Hypo_S281_Aligned.sortedByCoord.out.bam" | "output.STAR.3_G3_Norm_S303_Aligned.sortedByCoord.out.bam" | "output.STAR.3_H7_Hypo_S373_Aligned.sortedByCoord.out.bam" | "output.STAR.4_A12_Hypo_S48_Aligned.sortedByCoord.out.bam" | "output.STAR.4_A3_Norm_S21_Aligned.sortedByCoord.out.bam" | "output.STAR.4_A5_Norm_S23_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | ... | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 |
| mean | 0.012253 | 1.024636 | 2.240734 | 0.989971 | 3.810325 | 99.728133 | 5.728046 | 5.588689 | 9.081146 | 0.005625 | ... | 0.006671 | 0.018401 | 0.001308 | 25.948286 | 35.644981 | 0.389858 | 3.063966 | 56.798291 | 0.286561 | 34.692422 |
| std | 0.207726 | 6.097362 | 25.449630 | 14.975347 | 57.618692 | 1035.207158 | 52.387257 | 45.051444 | 43.536175 | 0.289519 | ... | 0.181321 | 1.417297 | 0.039599 | 260.431089 | 998.534500 | 3.654229 | 23.088568 | 463.501312 | 0.928592 | 679.960908 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 43.000000 | 3.000000 | 3.000000 | 4.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 32.000000 | 0.000000 | 0.000000 |
| max | 14.000000 | 289.000000 | 1707.000000 | 1267.000000 | 3816.000000 | 60475.000000 | 5960.000000 | 3631.000000 | 2418.000000 | 36.000000 | ... | 20.000000 | 180.000000 | 2.000000 | 21842.000000 | 142393.000000 | 251.000000 | 1637.000000 | 34790.000000 | 45.000000 | 81952.000000 |
8 rows × 70 columns
¶
2.1.1) Statistical study on mean and variances on cells
df_MCF7_unf_summary = df_MCF7_unf.describe()
¶
2.1.1.1) Study of mean on cells
We start studying the distribution of the means in the unfiltered dataset.
In particular, after watching the minimum and maximum value of them we study the histogram.
df_MCF7_unf_mean_max_value = df_MCF7_unf_summary.iloc[1].max()
print("Max value in unfiltered data:", df_MCF7_unf_mean_max_value)
df_MCF7_unf_mean_min_value = df_MCF7_unf_summary.iloc[1].min()
print("Min value in unfiltered data:", df_MCF7_unf_mean_min_value)
Max value in unfiltered data: 100.639094793756 Min value in unfiltered data: 4.360338362256911e-05
sns.displot(df_MCF7_unf_summary.iloc[1], bins=100)
plt.xlim(0, 105)
plt.ylim(0, 30)
plt.title("Distribution of mean in unfiltered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In unfiltered dataset means are mostly concentrated around in interval [0, 10] and on interval [20, 80] with some peaks around 30, 50 and 70.
Then we move over to filtered dataset and we apply same staregy done before
mcf_filtered_mean_max_value = mcf_filtered_summary.iloc[1].max()
print("Max value in filtered data:", mcf_filtered_mean_max_value)
mcf_filtered_mean_min_value = mcf_filtered_summary.iloc[1].min()
print("Min value in filtered data:", mcf_filtered_mean_min_value)
Max value in filtered data: 104.62063869094747 Min value in filtered data: 13.900395882818685
sns.displot(mcf_filtered_summary.iloc[1], bins=50)
plt.xlim(0, 105)
plt.ylim(0,20)
plt.title("Distribution of mean in filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
This time the lowest mean is 13.9 where there is the first peak. Then the distribution is non-zero on the entire interval. Focusing it is more dense in interval [40, 90] with some peaks on 40, 60 and 80
Finally, we move over to the dataset made of elements that are in unfiltered dataset but not in filtered (removed elements)
mcf_removed_elements_mean_max_value = mcf_removed_elements_summary.iloc[1].max()
print("Max value in removed elements:", mcf_removed_elements_mean_max_value)
mcf_removed_elements_mean_min_value = mcf_removed_elements_summary.iloc[1].min()
print("Min value in removed elements:", mcf_removed_elements_mean_min_value)
Max value in removed elements: 100.639094793756 Min value in removed elements: 4.360338362256911e-05
sns.displot(mcf_removed_elements_summary.iloc[1], bins=50)
plt.xlim(0, 105)
plt.ylim(0,50)
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Vast majority of removed elements has low mean, between 0 and 10
Going over we checked if elements with highest and lowest mean in unfiltered and filtered datasets were the same.
In case of negative answere, we checked if the element with highest or lowest mean in unfiltered are contained in filtered.
Outputs are as follow.
# is cell with highest mean value in filtered and unfiltered data the same?
max_mean_column_filtered = mcf_filtered.mean().idxmax()
max_mean_column_unfiltered = df_MCF7_unf.mean().idxmax()
print("is cell with highest mean value in filtered and unfiltered data the same?", max_mean_column_filtered==max_mean_column_unfiltered,
"\nmax mean column in filtered data:", max_mean_column_filtered, " and the value is:", mcf_filtered.mean().max(),
"\nmax mean column in unfiltered data:", max_mean_column_unfiltered, " and the value is:", df_MCF7_unf.mean().max())
is cell with highest mean value in filtered and unfiltered data the same? False max mean column in filtered data: "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" and the value is: 104.62063869094747 max mean column in unfiltered data: "output.STAR.2_C7_Hypo_S127_Aligned.sortedByCoord.out.bam" and the value is: 100.639094793756
# is cell with highest mean value in unfiltered contained in filtered?
if max_mean_column_unfiltered in mcf_filtered:
print("yes")
else:
print("no")
no
# is cell with lowest mean value in filtered and unfiltered data the same?
min_mean_column_filtered = mcf_filtered.mean().idxmin()
min_mean_column_unfiltered = df_MCF7_unf.mean().idxmin()
print("is cell with lowest mean value in filtered and unfiltered data the same?", min_mean_column_filtered==min_mean_column_unfiltered,
"\nmin mean column in filtered data:", min_mean_column_filtered, " and the value is:", mcf_filtered.mean().min(),
"\nmin mean column in unfiltered data:", min_mean_column_unfiltered, " and the value is:", df_MCF7_unf.mean().min())
is cell with lowest mean value in filtered and unfiltered data the same? False min mean column in filtered data: "output.STAR.1_E2_Norm_S194_Aligned.sortedByCoord.out.bam" and the value is: 13.900395882818685 min mean column in unfiltered data: "output.STAR.1_H1_Norm_S337_Aligned.sortedByCoord.out.bam" and the value is: 4.360338362256911e-05
# is cell with lowest mean value in unfiltered contained in filtered?
if min_mean_column_unfiltered in mcf_filtered:
print("yes")
else:
print("no")
no
¶
2.1.1.2) Study of STD
Now we apply same strategy done before (2.1.1.1) on STD
df_MCF7_unf_std_max_value = df_MCF7_unf_summary.iloc[2].max()
print("max STD value in unfiltered:", df_MCF7_unf_std_max_value)
df_MCF7_unf_std_min_value = df_MCF7_unf_summary.iloc[2].min()
print("min TD value in unfiltered:", df_MCF7_unf_std_min_value)
max STD value in unfiltered: 1668.9422310403038 min TD value in unfiltered: 0.006603285820148005
sns.displot(df_MCF7_unf_summary.iloc[2], bins=100)
plt.xlim(0, 1750)
plt.ylim(0,50)
plt.title("Distribution of STD in unfiltered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
STD in unfiltered dataset are mainly distributed in the interval [0, 1000] with peaks on 0, 250, 400, 600 and 1000
mcf_filtered_std_max_value = mcf_filtered_summary.iloc[2].max()
print("max STD value in filtered:", mcf_filtered_std_max_value)
mcf_filtered_std_min_value = mcf_filtered_summary.iloc[2].min()
print("min STD value in filtered:", df_MCF7_unf_std_min_value)
max STD value in filtered: 1835.9683350446378 min STD value in filtered: 0.006603285820148005
sns.displot(mcf_filtered_summary.iloc[2], bins=100)
plt.xlim(0, 2000)
plt.ylim(0,25)
plt.title("Distribution of STD in filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In filtered dataset STD are distributed mainly on interval [50, 700], with peaks on 300, 400 and 600; moreover there is a sligh distribution on (700, 1200]
mcf_removed_elements_std_max_value = mcf_removed_elements_summary.iloc[2].max()
print("max STD value in removed elements:", mcf_removed_elements_std_max_value)
mcf_removed_elements_std_min_value = mcf_removed_elements_summary.iloc[2].min()
print("min STD value in removed elements:", mcf_removed_elements_std_min_value)
max STD value in removed elements: 1563.2626742257273 min STD value in removed elements: 0.006603285820148005
sns.displot(mcf_removed_elements_summary.iloc[2], bins=100)
plt.xlim(0, 1750)
plt.ylim(0,50)
plt.title("Distribution of STD in removed elements")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Also this time in removed elements, STD is concentrated mostly around 0
# is cell with highest std value in filtered and unfiltered data the same?
max_std_column_filtered = mcf_filtered.std().idxmax()
max_std_column_unfiltered = df_MCF7_unf.std().idxmax()
print("is cell with max std value in filtered and unfiltered data the same?", max_std_column_filtered==max_std_column_unfiltered,
"\nmin std column in filtered data:", max_std_column_filtered, " and the value is:", mcf_filtered.std().max(),
"\nmin std column in unfiltered data:", max_std_column_unfiltered, " and the value is:", df_MCF7_unf.std().max())
is cell with max std value in filtered and unfiltered data the same? True min std column in filtered data: "output.STAR.1_H12_Hypo_S366_Aligned.sortedByCoord.out.bam" and the value is: 1835.968335044638 min std column in unfiltered data: "output.STAR.1_H12_Hypo_S366_Aligned.sortedByCoord.out.bam" and the value is: 1668.9422310403038
# is cell with max std value in unfiltered contained in filtered?
if max_std_column_unfiltered in mcf_filtered:
print("yes")
else:
print("no")
yes
# is cell with lowest std value in filtered and unfiltered data the same?
min_std_column_filtered = mcf_filtered.std().idxmin()
min_std_column_unfiltered = df_MCF7_unf.std().idxmin()
print("is cell with lowest std value in filtered and unfiltered data the same?", min_std_column_filtered==min_std_column_unfiltered,
"\nmin std column in filtered data:", min_std_column_filtered, " and the value is:", mcf_filtered.std().min(),
"\nmin std column in unfiltered data:", min_std_column_unfiltered, " and the value is:", df_MCF7_unf.std().min())
is cell with lowest std value in filtered and unfiltered data the same? False min std column in filtered data: "output.STAR.1_C1_Norm_S97_Aligned.sortedByCoord.out.bam" and the value is: 72.76143736279771 min std column in unfiltered data: "output.STAR.1_H1_Norm_S337_Aligned.sortedByCoord.out.bam" and the value is: 0.006603285820148005
# is cell with lowest std value in unfiltered contained in filtered?
if min_std_column_unfiltered in mcf_filtered:
print("yes")
else:
print("no")
no
¶
2.1.1.3) DETAILS OF DISTRIBUTION OF MEANS
Continuing the study done before, we continue analizing the distribution of mean with the function describe()
df_MCF7_unf_summary.iloc[1].describe()
count 383.000000 mean 43.368444 std 23.998135 min 0.000044 25% 26.107548 50% 49.242784 75% 61.421405 max 100.639095 Name: mean, dtype: float64
In unfiltered dataset mean of means is 43.38, slightly lower than the median (49.24), suggesting that the symmetry of distribution is shifted toward left.
Given minimum equal to 0 and maximum equal to 100, the expected 25%-quantile, 50%-quantile and 75%-quantile in case of uniform distribution would have been 25, 50 and 75 while in this case are 25, 49 and 61, suggesting that many elements are concentrated in the interval of lenght between 50 and 60 and few of them in the interval of lenght 40 between 60 and 100
mcf_filtered_summary.iloc[1].describe()
count 313.000000 mean 61.114583 std 20.920048 min 13.900396 25% 46.456638 50% 63.289047 75% 77.084930 max 104.620639 Name: mean, dtype: float64
In filtered dataset the median is closer to the mean than what happens in unfiltered, implying that the distribution should be more symmetrical.
Given minimum value equal to 14 and maximum equal to 104 the expected 25%-quantile,50%-quantile and 75%-quantile in case of uniform distribution would have been 36.5, 65 and 89, while in this case are 46, 63 and 77, suggesting that implying that 50% of elements is in the 31 length interval [46,77] and the other 50% in the 59 length interval [14,46]U[77,104].
Furthermore, STD from unfiltered to filtered decreases, implying that the distribution in filtered is less spread than before.
mcf_removed_elements_summary.iloc[1].describe()
count 70.000000 mean 11.505886 std 24.024986 min 0.000044 25% 0.264237 50% 3.132489 75% 6.413818 max 100.639095 Name: mean, dtype: float64
In removed elememts all statistics confirm what said before that majority of elements have low mean.
¶
2.1.1.4) DETAILS OF DISTRIBUTION OF STD
We apply same strategy as before (2.2.3) on STD
df_MCF7_unf_summary.iloc[2].describe()
count 383.000000 mean 420.606147 std 287.762585 min 0.006603 25% 230.616936 50% 398.218463 75% 570.957846 max 1668.942231 Name: std, dtype: float64
mcf_filtered_summary.iloc[2].describe()
count 313.000000 mean 533.541022 std 267.786954 min 72.761437 25% 355.653903 50% 483.889437 75% 646.890817 max 1835.968335 Name: std, dtype: float64
From unfiltered to filtered, the mean STD increases from 420 to 533 implying that datas are more spread.
mcf_removed_elements_summary.iloc[2].describe()
count 70.000000 mean 130.276923 std 293.139183 min 0.006603 25% 1.890654 50% 26.661737 75% 54.779254 max 1563.262674 Name: std, dtype: float64
¶
2.1.2) STUDY ON genes by mean and variances on genes
We procede to do same studies done before (see 2.1.1)
df_MCF7_unf_transposed = df_MCF7_unf.T
print("UNFILTERED TRANSPOSED dimensions:", np.shape(df_MCF7_unf_transposed))
df_MCF7_unf_transposed_summary=df_MCF7_unf_transposed.describe()
UNFILTERED TRANSPOSED dimensions: (383, 22934)
mcf_filtered_transposed = mcf_filtered.T
print("FILTERED TRANSPOSED dimensions:", np.shape(mcf_filtered_transposed))
mcf_filtered_transposed_summary=mcf_filtered_transposed.describe()
mcf_filtered_transposed.describe()
FILTERED TRANSPOSED dimensions: (313, 18945)
| "WASH7P" | "MIR6859-1" | "WASH9P" | "OR4F29" | "MTND1P23" | "MTND2P28" | "MTCO1P12" | "MTCO2P12" | "MTATP8P1" | "MTATP6P1" | ... | "MT-TH" | "MT-TS2" | "MT-TL2" | "MT-ND5" | "MT-ND6" | "MT-TE" | "MT-CYB" | "MT-TT" | "MT-TP" | "MAFIP" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 313.000000 | 313.000000 | 313.000000 | 313.000000 | 313.000000 | 313.000000 | 313.000000 | 313.000000 | 313.000000 | 313.000000 | ... | 313.000000 | 313.000000 | 313.000000 | 313.000000 | 313.000000 | 313.000000 | 313.000000 | 313.000000 | 313.000000 | 313.000000 |
| mean | 0.156550 | 0.031949 | 1.587859 | 0.054313 | 0.057508 | 7.067093 | 5.300319 | 0.607029 | 0.083067 | 251.246006 | ... | 1.194888 | 0.517572 | 3.642173 | 1157.523962 | 231.782748 | 5.734824 | 2723.453674 | 2.463259 | 6.418530 | 2.012780 |
| std | 0.673036 | 0.275498 | 2.358722 | 0.299982 | 0.246546 | 7.083203 | 5.792283 | 1.026359 | 0.309260 | 244.053050 | ... | 2.046817 | 1.168707 | 4.307835 | 899.677268 | 196.936662 | 6.539571 | 2862.158823 | 3.532735 | 7.170133 | 3.867165 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 48.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 429.000000 | 90.000000 | 1.000000 | 421.000000 | 0.000000 | 1.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 5.000000 | 3.000000 | 0.000000 | 0.000000 | 178.000000 | ... | 0.000000 | 0.000000 | 2.000000 | 1027.000000 | 191.000000 | 4.000000 | 1360.000000 | 1.000000 | 4.000000 | 0.000000 |
| 75% | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 12.000000 | 9.000000 | 1.000000 | 0.000000 | 408.000000 | ... | 2.000000 | 1.000000 | 6.000000 | 1667.000000 | 325.000000 | 9.000000 | 4615.000000 | 4.000000 | 9.000000 | 3.000000 |
| max | 9.000000 | 4.000000 | 20.000000 | 3.000000 | 2.000000 | 41.000000 | 31.000000 | 6.000000 | 2.000000 | 1491.000000 | ... | 15.000000 | 8.000000 | 20.000000 | 4835.000000 | 1334.000000 | 40.000000 | 16026.000000 | 22.000000 | 42.000000 | 30.000000 |
8 rows × 18945 columns
mcf_removed_elements_transposed=mcf_removed_elements.T
print("REMOVED DATA dimensions:", np.shape(mcf_removed_elements_transposed))
mcf_removed_elements_transposed_summary=mcf_removed_elements_transposed.describe()
mcf_removed_elements_transposed.describe()
REMOVED DATA dimensions: (70, 22934)
| "WASH7P" | "MIR6859-1" | "WASH9P" | "OR4F29" | "MTND1P23" | "MTND2P28" | "MTCO1P12" | "MTCO2P12" | "MTATP8P1" | "MTATP6P1" | ... | "MT-TH" | "MT-TS2" | "MT-TL2" | "MT-ND5" | "MT-ND6" | "MT-TE" | "MT-CYB" | "MT-TT" | "MT-TP" | "MAFIP" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 70.000000 | 70.0 | 70.000000 | 70.000000 | 70.000000 | 70.000000 | 70.000000 | 70.000000 | 70.000000 | 70.000000 | ... | 70.000000 | 70.000000 | 70.000000 | 70.000000 | 70.000000 | 70.000000 | 70.000000 | 70.000000 | 70.000000 | 70.000000 |
| mean | 0.028571 | 0.0 | 0.257143 | 0.057143 | 0.014286 | 2.657143 | 1.914286 | 0.157143 | 0.028571 | 91.528571 | ... | 0.457143 | 0.114286 | 0.928571 | 385.800000 | 83.057143 | 1.985714 | 816.771429 | 0.385714 | 2.085714 | 0.571429 |
| std | 0.239046 | 0.0 | 1.125279 | 0.376318 | 0.119523 | 8.597077 | 7.338094 | 0.628680 | 0.239046 | 302.238255 | ... | 1.594244 | 0.525932 | 3.271845 | 1219.654758 | 275.956145 | 6.275078 | 2675.968573 | 1.721921 | 8.019438 | 3.828086 |
| min | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 4.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 32.000000 | 5.000000 | 0.000000 | 40.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 20.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 88.500000 | 14.000000 | 1.000000 | 235.500000 | 0.000000 | 0.000000 | 0.000000 |
| max | 2.000000 | 0.0 | 7.000000 | 3.000000 | 1.000000 | 45.000000 | 36.000000 | 4.000000 | 2.000000 | 1662.000000 | ... | 10.000000 | 4.000000 | 22.000000 | 8115.000000 | 2002.000000 | 46.000000 | 15766.000000 | 13.000000 | 56.000000 | 32.000000 |
8 rows × 22934 columns
¶
2.1.2.1) Mean
df_MCF7_unf_transposed_mean_max_value = df_MCF7_unf_transposed_summary.iloc[1].max()
print("Max mean value in unfiltered transposed data:", df_MCF7_unf_transposed_mean_max_value)
df_MCF7_unf_transposed_mean_min_value = df_MCF7_unf_transposed_summary.iloc[1].min()
print("Min mean value in unfiltered transposed data:", df_MCF7_unf_transposed_mean_min_value)
Max mean value in unfiltered transposed data: 23052.762402088774 Min mean value in unfiltered transposed data: 0.005221932114882507
sns.displot(df_MCF7_unf_transposed_summary.iloc[1], bins=500)
plt.xlim(0, 27000)
plt.ylim(0, 1000)
plt.title("Distribution of mean in unfiltered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Histogram of distribution of means in genes in unfiltered elemements say that almost all elements have mean close to 0
mcf_filtered_transposed_mean_max_value = mcf_filtered_transposed_summary.iloc[1].max()
print("Max mean value in filtered transposed data:", mcf_filtered_transposed_mean_max_value)
mcf_filtered_transposed_mean_min_value = mcf_filtered_transposed_summary.iloc[1].min()
print("Min mean value in filtered transposed data:", mcf_filtered_transposed_mean_min_value)
Max mean value in filtered transposed data: 26918.849840255592 Min mean value in filtered transposed data: 0.019169329073482427
sns.displot(mcf_filtered_transposed_summary.iloc[1], bins=500)
plt.xlim(0, 27000)
plt.ylim(0, 1000)
plt.title("Distribution of mean in filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Histogram of distribution of means in genes in filtered elemements say that almost all elements have mean close to 0
mcf_removed_elements_transposed_mean_max_value = mcf_removed_elements_transposed_summary.iloc[1].max()
print("Max value in removed elements transposed data:", mcf_removed_elements_transposed_mean_max_value)
mcf_removed_elements_transposed_mean_min_value = mcf_removed_elements_transposed_summary.iloc[1].min()
print("Min value in removed elements transposed data:", mcf_removed_elements_transposed_mean_min_value)
Max value in removed elements transposed data: 6087.428571428572 Min value in removed elements transposed data: 0.0
sns.displot(mcf_removed_elements_transposed_summary.iloc[1], bins=500)
plt.xlim(0, 6100)
plt.ylim(0, 1000)
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Histogram of distribution of means in genes in removed elemements say that almost all elements have mean close to 0
# is cell with max mean value in filtered_tranposed and unfiltered_transposed data the same?
max_mean_column_filtered_transposed = mcf_filtered_transposed.mean().idxmin()
max_mean_column_unfiltered_transposed = df_MCF7_unf_transposed.mean().idxmin()
print("is cell with max mean value in filtered transposed and unfiltered transposed data the same?", max_mean_column_filtered_transposed==max_mean_column_unfiltered_transposed,
"\nmax mean column in filtered data:", max_mean_column_filtered_transposed, " and the value is:", mcf_filtered_transposed.mean().max(),
"\nmax mean column in unfiltered data:", max_mean_column_unfiltered_transposed, " and the value is:", df_MCF7_unf_transposed.mean().max())
is cell with max mean value in filtered transposed and unfiltered transposed data the same? False max mean column in filtered data: "MIR6726" and the value is: 26918.849840255592 max mean column in unfiltered data: "CICP3" and the value is: 23052.762402088774
# is cell with highest mean value in unfiltered contained in filtered?
if max_mean_column_unfiltered_transposed in mcf_filtered_transposed:
print("yes")
else:
print("no")
no
# is cell with lowest mean value in filtered_tranposed and unfiltered_transposed data the same?
min_mean_column_filtered_transposed = mcf_filtered_transposed.mean().idxmin()
min_mean_column_unfiltered_transposed = df_MCF7_unf_transposed.mean().idxmin()
print("is cell with lowest mean value in filtered transposed and unfiltered transposed data the same?", min_mean_column_filtered_transposed==min_mean_column_unfiltered_transposed,
"\nmin mean column in filtered data:", min_mean_column_filtered_transposed, " and the value is:", mcf_filtered_transposed.mean().min(),
"\nmin mean column in unfiltered data:", min_mean_column_unfiltered_transposed, " and the value is:", df_MCF7_unf_transposed.mean().min())
is cell with lowest mean value in filtered transposed and unfiltered transposed data the same? False min mean column in filtered data: "MIR6726" and the value is: 0.019169329073482427 min mean column in unfiltered data: "CICP3" and the value is: 0.005221932114882507
# is cell with lowest mean value in unfiltered contained in filtered?
if min_mean_column_unfiltered_transposed in mcf_filtered_transposed:
print("yes")
else:
print("no")
no
¶
2.1.2.2) STD
df_MCF7_unf_transposed_std_max_value = df_MCF7_unf_transposed_summary.iloc[2].max()
print("Max std value in unfiltered transposed data:", df_MCF7_unf_transposed_std_max_value)
df_MCF7_unf_transposed_std_min_value = df_MCF7_unf_transposed_summary.iloc[2].min()
print("Min std value in unfiltered transposed data:", df_MCF7_unf_transposed_std_min_value)
Max std value in unfiltered transposed data: 27101.548019606067 Min std value in unfiltered transposed data: 0.07216829037412606
sns.displot(df_MCF7_unf_transposed_summary.iloc[2], bins=500)
plt.xlim(0, 27200)
plt.ylim(0, 1000)
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Histogram of distribution of STD in genes in unfiltered elemements say that almost all elements have mean close to 0
mcf_filtered_transposed_std_max_value = mcf_filtered_transposed_summary.iloc[2].max()
print("Max std value in filtered transposed data:", mcf_filtered_transposed_mean_max_value)
mcf_filtered_transposed_std_min_value = mcf_filtered_transposed_summary.iloc[2].min()
print("Min std value in filtered transposed data:", mcf_filtered_transposed_mean_min_value)
Max std value in filtered transposed data: 26918.849840255592 Min std value in filtered transposed data: 0.019169329073482427
sns.displot(mcf_filtered_transposed_summary.iloc[2], bins=1000)
plt.xlim(0, 27000)
plt.ylim(0, 1000)
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Histogram of distribution of STD in genes in filtered elemements say that almost all elements have mean close to 0
mcf_removed_elements_transposed_std_max_value = mcf_removed_elements_transposed_summary.iloc[2].max()
print("Max value std in removed elements transposed data:", mcf_removed_elements_transposed_mean_max_value)
mcf_removed_elements_transposed_mean_min_value = mcf_removed_elements_transposed_summary.iloc[2].min()
print("Min value std in removed elements transposed data:", mcf_removed_elements_transposed_mean_min_value)
Max value std in removed elements transposed data: 6087.428571428572 Min value std in removed elements transposed data: 0.0
sns.displot(mcf_removed_elements_transposed_summary.iloc[2], bins=1000)
plt.xlim(0, 6100)
plt.ylim(0, 3000)
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Histogram of distribution of STD in genes in removed elemements say that almost all elements have mean close to 0
# is cell with max mean value in filtered_tranposed and unfiltered_transposed data the same?
max_std_column_filtered_transposed = mcf_filtered_transposed.std().idxmin()
max_std_column_unfiltered_transposed = df_MCF7_unf_transposed.std().idxmin()
print("is cell with max std value in filtered transposed and unfiltered transposed data the same?", max_std_column_filtered_transposed==max_std_column_unfiltered_transposed,
"\nmax std column in filtered data:", max_std_column_filtered_transposed, " and the value is:", mcf_filtered_transposed.std().max(),
"\nmax std column in unfiltered data:", max_std_column_unfiltered_transposed, " and the value is:", df_MCF7_unf_transposed.std().max())
is cell with max std value in filtered transposed and unfiltered transposed data the same? False max std column in filtered data: "SDHCP2" and the value is: 27449.497138097668 max std column in unfiltered data: "ROR1-AS1" and the value is: 27101.54801960607
# is cell with highest mean value in unfiltered contained in filtered?
if max_std_column_unfiltered_transposed in mcf_filtered_transposed:
print("yes")
else:
print("no")
no
# is cell with lowest std value in filtered_tranposed and unfiltered_transposed data the same?
min_std_column_filtered_transposed = mcf_filtered_transposed.std().idxmin()
min_std_column_unfiltered_transposed = df_MCF7_unf_transposed.std().idxmin()
print("is cell with lowest std value in filtered transposed and unfiltered transposed data the same?", min_std_column_filtered_transposed==min_std_column_unfiltered_transposed,
"\nmin std column in filtered data:", min_std_column_filtered_transposed, " and the value is:", mcf_filtered_transposed.std().min(),
"\nmin std column in unfiltered data:", min_std_column_unfiltered_transposed, " and the value is:", df_MCF7_unf_transposed.std().min())
is cell with lowest std value in filtered transposed and unfiltered transposed data the same? False min std column in filtered data: "SDHCP2" and the value is: 0.13733946369142572 min std column in unfiltered data: "ROR1-AS1" and the value is: 0.07216829037412606
# is cell with lowest mean value in unfiltered contained in filtered?
if min_std_column_unfiltered_transposed in mcf_filtered_transposed:
print("yes")
else:
print("no")
no
¶
2.1.2.3) Details on distribution of mean
df_MCF7_unf_transposed_summary.iloc[1].describe()
count 22934.000000 mean 43.368444 std 352.550337 min 0.005222 25% 0.133159 50% 3.755875 75% 29.183420 max 23052.762402 Name: mean, dtype: float64
All statistics confirm what seen before in the histogram: almost all elements are concentrated around 0.
mcf_filtered_transposed_summary.iloc[1].describe()
count 18945.000000 mean 61.114583 std 451.021087 min 0.019169 25% 0.875399 50% 10.297125 75% 45.626198 max 26918.849840 Name: mean, dtype: float64
All statistics confirm what seen before in the histogram: almost all elements are concentrated around 0.
mcf_removed_elements_transposed_summary.iloc[1].describe()
count 22934.000000 mean 11.505886 std 97.402068 min 0.000000 25% 0.014286 50% 0.771429 75% 6.785714 max 6087.428571 Name: mean, dtype: float64
All statistics confirm what seen before in the histogram: almost all elements are concentrated around 0.
¶
2.1.2.4) Details of distribution of std
df_MCF7_unf_transposed_summary.iloc[2].describe()
count 22934.000000 mean 50.400187 std 365.511266 min 0.072168 25% 0.985722 50% 9.557268 75% 40.101361 max 27101.548020 Name: std, dtype: float64
mcf_filtered_transposed_summary.iloc[2].describe()
count 18945.000000 mean 59.061355 std 387.511937 min 0.137339 25% 3.331064 50% 17.175465 75% 49.605087 max 27449.497138 Name: std, dtype: float64
From unfiltered to filtered datasets mean of STD increases, implying that datas are more spread.
mcf_removed_elements_transposed_summary.iloc[2].describe()
count 22934.000000 mean 35.803314 std 304.662540 min 0.000000 25% 0.119523 50% 3.421767 75% 24.840822 max 19524.700983 Name: std, dtype: float64
¶
2.1.3) TRIAL AND ERROR OF PSEUDO FILTERING ON CELLS
Now, to understand how filtering proces happen we try to put upper/lower bounds to mean and STD.
To check the similarity between pseudo filtered dataset and filtered one we used the ratio between number of elements in intersection between pseudo filtered and filtered and number of elements in filtered.
Watch also number of removed element at each iteration, elements wrongly filtered (not in filtered but selected in pseudo filtered) and elements that have not been filtered (in filtered but not in pseudo filtered).
# similarity between filtered and unfiltred data
filtered_set = set(mcf_filtered.columns)
unfiltered_set = set(df_MCF7_unf.columns)
intersection = filtered_set.intersection(unfiltered_set)
union = filtered_set.union(unfiltered_set)
similarity = len(intersection) / len(union)
removed = unfiltered_set - filtered_set
print(f"Similarity Percentage: {similarity}%", "\nnumber of removed:", len(removed))
Similarity Percentage: 0.8172323759791122% number of removed: 70
# trial 1: filter removing cells with mean below 11 and std below 60
# Filter columns based on mean and standard deviation
filtered_columns = df_MCF7_unf.columns[(df_MCF7_unf.mean() >= 11) & (df_MCF7_unf.std() >= 60)]
# Create pseudo_filtered dataset
pseudo_filtered = df_MCF7_unf[filtered_columns]
pseudo_filtered_summary=pseudo_filtered.describe()
pseudo_filtered.describe()
| "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B10_Hypo_S76_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B11_Hypo_S77_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B12_Hypo_S78_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | ... | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 |
| mean | 40.817651 | 86.442400 | 14.531351 | 56.213613 | 75.397183 | 62.767725 | 67.396747 | 99.728133 | 70.469957 | 41.026206 | ... | 17.362562 | 42.080230 | 34.692422 | 32.735284 | 21.992718 | 17.439391 | 49.242784 | 61.545609 | 68.289352 | 62.851400 |
| std | 465.709940 | 1036.572689 | 123.800530 | 503.599145 | 430.471519 | 520.167576 | 459.689019 | 1035.207158 | 828.757699 | 369.992969 | ... | 193.153757 | 256.775704 | 679.960908 | 300.291051 | 153.441647 | 198.179666 | 359.337479 | 540.847355 | 636.892085 | 785.670341 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 17.000000 | 5.000000 | 7.000000 | 23.000000 | 39.000000 | 35.000000 | 38.000000 | 43.000000 | 32.000000 | 23.000000 | ... | 9.000000 | 30.000000 | 0.000000 | 17.000000 | 12.000000 | 9.000000 | 27.000000 | 30.000000 | 38.000000 | 33.000000 |
| max | 46744.000000 | 82047.000000 | 10582.000000 | 46856.000000 | 29534.000000 | 50972.000000 | 36236.000000 | 60475.000000 | 56068.000000 | 24994.000000 | ... | 17800.000000 | 23355.000000 | 81952.000000 | 29540.000000 | 12149.000000 | 19285.000000 | 28021.000000 | 40708.000000 | 46261.000000 | 68790.000000 |
8 rows × 324 columns
pseudo_filtered_mean_max_value = pseudo_filtered_summary.iloc[1].max()
print("max mean value in pseudo filtered:", pseudo_filtered_mean_max_value)
mcf_filtered_mean_min_value = pseudo_filtered_summary.iloc[1].min()
print("min mean value in pseudo filtered:", mcf_filtered_mean_min_value)
max mean value in pseudo filtered: 100.639094793756 min mean value in pseudo filtered: 11.483779541292405
sns.displot(pseudo_filtered_summary.iloc[1], bins=100)
plt.xlim(0, 105)
plt.ylim(0, 20)
plt.title("Distribution of means in pseudo filtered datased based on cells")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
pseudo_filtered_std_max_value = pseudo_filtered_summary.iloc[2].max()
print("max STD value in pseudo filtered:", pseudo_filtered_std_max_value)
mcf_filtered_std_min_value = pseudo_filtered_summary.iloc[2].min()
print("min STD value in pseudo filtered:", mcf_filtered_std_min_value)
max STD value in pseudo filtered: 1668.9422310403038 min STD value in pseudo filtered: 66.39211740865552
sns.displot(pseudo_filtered_summary.iloc[2], bins=100)
plt.xlim(0, 1700)
plt.ylim(0, 20)
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
# similarity between filtered and pseudo_filtered data
# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set = set(pseudo_filtered.columns)
unfiltered_set = set(df_MCF7_unf.columns)
# Calculate the intersection and union of the sets
intersection = filtered_set.intersection(pseudo_set)
union = filtered_set.union(pseudo_set)
# Calculate the Jaccard similarity coefficient
similarity = len(intersection) / len(union)
# Convert the similarity to percentage
similarity_percentage = similarity * 100
rem=unfiltered_set - pseudo_set
diff = pseudo_set - filtered_set #elements in pseudo_filtered that are not in filtered
diff1 = filtered_set - pseudo_set #elements in filtered that are not in pseudo_filtered
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage}%", "\nnumber of removed", len(rem),
"\nnumber of elements in pseudo_filtered that are not in filtered:", len(diff),
"\nnumber of elemtns in filtered that are not in pseudo_filtered:", len(diff1))
Similarity Percentage: 96.60493827160494% number of removed 59 number of elements in pseudo_filtered that are not in filtered: 11 number of elemtns in filtered that are not in pseudo_filtered: 0
Given these lower bounds check if there are betters (check if similarity percentage increases or decreases)
#check 1: reduce mean to 9
# Filter columns based on mean and standard deviation
filtered_columns_check = df_MCF7_unf.columns[(df_MCF7_unf.mean() >= 9)]
# Create pseudo_filtered dataset
pseudo_filtered_check = df_MCF7_unf[filtered_columns_check]
pseudo_filtered_check_summary=pseudo_filtered_check.describe()
pseudo_filtered_check.describe()
# similarity between filtered and pseudo_filtred data
# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set_check = set(pseudo_filtered_check.columns)
unfiltered_set = set(df_MCF7_unf.columns)
# Calculate the intersection and union of the sets
intersection_check = filtered_set.intersection(pseudo_set_check)
union_check = filtered_set.union(pseudo_set_check)
# Calculate the Jaccard similarity coefficient
similarity_check = len(intersection_check) / len(union_check)
# Convert the similarity to percentage
similarity_percentage_check = similarity_check * 100
rem_check=unfiltered_set - pseudo_set_check
diff__check = pseudo_set - filtered_set #elements in pseudo_filtred that are not in filtered
diff1__check = filtered_set - pseudo_set_check #elements in filtered that are not in pseudo_filetred
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage_check}%",
"\nnumber of removed", len(rem_check),
"\nnumber of elements in pseudo_filtred that are not in filtered (elements wrongly filtered):", len(diff__check),
"\nnumber of elemetns in filtered that are not in pseudo_filtered (elements that still need to filter):", len(diff1__check))
Similarity Percentage: 96.0122699386503% number of removed 57 number of elements in _check that are not in filtered: 11 number of elemtns in filtered that are not in _check: 0
Similarity Percentage: 96.0122699386503% number of removed 57 number of elements in _check that are not in filtered: 11 number of elemtns in filtered that are not in pseudo_filtered: 0
#check 2: reduce std to 40
# Filter columns based on mean and standard deviation
filtered_columns_check2 = df_MCF7_unf.columns[(df_MCF7_unf.std() >= 50)]
# Create pseudo_filtered dataset
pseudo_filtered_check2 = df_MCF7_unf[filtered_columns_check2]
pseudo_filtered_check2_summary=pseudo_filtered_check2.describe()
# similarity between filtered and _check data
# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set_check2 = set(pseudo_filtered_check2.columns)
unfiltered_set = set(df_MCF7_unf.columns)
# Calculate the intersection and union of the sets
intersection_check2 = filtered_set.intersection(pseudo_set_check2)
union_check2 = filtered_set.union(pseudo_set_check2)
similarity_check2 = len(intersection_check2) / len(union_check2)
# Convert the similarity to percentage
similarity_percentage_check2 = similarity_check2 * 100
rem_check2=unfiltered_set - pseudo_set_check2
diff__check2 = pseudo_set - filtered_set #elements in _check that are not in filtered
diff1__check2 = filtered_set - pseudo_set_check2 #elements in filtered that are not in _check
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage_check2}%", "\nnumber of removed", len(rem_check2),
"\nnumber of elements in _check that are not in filtered:", len(diff__check2),
"\nnumber of elemtns in filtered that are not in _check:", len(diff1__check2))
Similarity Percentage: 93.7125748502994% number of removed 49 number of elements in _check that are not in filtered: 11 number of elemtns in filtered that are not in _check: 0
#check 3: increase mean to 12
# Filter columns based on mean and standard deviation
filtered_columns_check3 = df_MCF7_unf.columns[(df_MCF7_unf.mean() >= 12)]
# Create pseudo_filtered dataset
pseudo_filtered_check3 = df_MCF7_unf[filtered_columns_check3]
pseudo_filtered_check3_summary=pseudo_filtered_check3.describe()
# similarity between filtered and _check data
# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set_check3 = set(pseudo_filtered_check3.columns)
unfiltered_set = set(df_MCF7_unf.columns)
# Calculate the intersection and union of the sets
intersection_check3 = filtered_set.intersection(pseudo_set_check3)
union_check3 = filtered_set.union(pseudo_set_check3)
# Calculate the Jaccard similarity coefficient
similarity_check3 = len(intersection_check3) / len(union_check3)
# Convert the similarity to percentage
similarity_percentage_check3 = similarity_check3 * 100
rem_check3=unfiltered_set - pseudo_set_check3
diff__check3 = pseudo_set - filtered_set #elements in _check that are not in filtered
diff1__check3 = filtered_set - pseudo_set_check3 #elements in filtered that are not in _check
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage_check3}%", "\nnumber of removed", len(rem_check3),
"\nnumber of elements in _check that are not in filtered:", len(diff__check3),
"\nnumber of elemtns in filtered that are not in _check:", len(diff1__check3))
# I removed 66 elements. 7 of these are mistakes. still need to remove 11 elements
Similarity Percentage: 95.67901234567901% number of removed 62 number of elements in _check that are not in filtered: 11 number of elemtns in filtered that are not in _check: 3
#check 4: increase std to 70
# Filter columns based on mean and standard deviation
filtered_columns_check3 = df_MCF7_unf.columns[(df_MCF7_unf.std() >= 70)]
# Create pseudo_filtered dataset
pseudo_filtered_check3 = df_MCF7_unf[filtered_columns_check3]
pseudo_filtered_check3_summary=pseudo_filtered_check3.describe()
# similarity between filtered and _check data
# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set_check3 = set(pseudo_filtered_check3.columns)
unfiltered_set = set(df_MCF7_unf.columns)
# Calculate the intersection and union of the sets
intersection_check3 = filtered_set.intersection(pseudo_set_check3)
union_check3 = filtered_set.union(pseudo_set_check3)
# Calculate the Jaccard similarity coefficient
similarity_check3 = len(intersection_check3) / len(union_check3)
# Convert the similarity to percentage
similarity_percentage_check3 = similarity_check3 * 100
rem_check3=unfiltered_set - pseudo_set_check3
diff__check3 = pseudo_set - filtered_set #elements in _check that are not in filtered
diff1__check3 = filtered_set - pseudo_set_check3 #elements in filtered that are not in _check
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage_check3}%", "\nnumber of removed", len(rem_check3),
"\nnumber of elements in _check that are not in filtered:", len(diff__check3),
"\nnumber of elemtns in filtered that are not in _check:", len(diff1__check3))
Similarity Percentage: 95.41284403669725% number of removed 57 number of elements in _check that are not in filtered: 11 number of elemtns in filtered that are not in _check: 1
THUS MEAN=11 annd STD=60 are best LOWER BOUNDS
# trial 2: remove elements with mean over 88
# since element with max std in unfiltered is also in filtered, I will not put an upper bound on std
# Filter columns based on mean and standard deviation
filtered_columns2 = pseudo_filtered.columns[(pseudo_filtered.mean() < 88)]
# Create pseudo_filtered dataset
pseudo_filtered2= df_MCF7_unf[filtered_columns2]
pseudo_filtered2_summary=pseudo_filtered2.describe()
pseudo_filtered2.describe()
| "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" | "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B11_Hypo_S77_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B12_Hypo_S78_Aligned.sortedByCoord.out.bam" | "output.STAR.1_B4_Norm_S52_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" | "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | ... | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 | 22934.000000 |
| mean | 40.817651 | 86.442400 | 14.531351 | 56.213613 | 75.397183 | 62.767725 | 67.396747 | 70.469957 | 41.026206 | 13.533793 | ... | 17.362562 | 42.080230 | 34.692422 | 32.735284 | 21.992718 | 17.439391 | 49.242784 | 61.545609 | 68.289352 | 62.851400 |
| std | 465.709940 | 1036.572689 | 123.800530 | 503.599145 | 430.471519 | 520.167576 | 459.689019 | 828.757699 | 369.992969 | 146.444225 | ... | 193.153757 | 256.775704 | 679.960908 | 300.291051 | 153.441647 | 198.179666 | 359.337479 | 540.847355 | 636.892085 | 785.670341 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 17.000000 | 5.000000 | 7.000000 | 23.000000 | 39.000000 | 35.000000 | 38.000000 | 32.000000 | 23.000000 | 5.000000 | ... | 9.000000 | 30.000000 | 0.000000 | 17.000000 | 12.000000 | 9.000000 | 27.000000 | 30.000000 | 38.000000 | 33.000000 |
| max | 46744.000000 | 82047.000000 | 10582.000000 | 46856.000000 | 29534.000000 | 50972.000000 | 36236.000000 | 56068.000000 | 24994.000000 | 13587.000000 | ... | 17800.000000 | 23355.000000 | 81952.000000 | 29540.000000 | 12149.000000 | 19285.000000 | 28021.000000 | 40708.000000 | 46261.000000 | 68790.000000 |
8 rows × 320 columns
pseudo_filtered2_mean_max_value = pseudo_filtered2_summary.iloc[1].max()
print("max mean value in pseudo filtered2:", pseudo_filtered2_mean_max_value)
mcf_filtered2_mean_min_value = pseudo_filtered2_summary.iloc[1].min()
print("min mean value in pseudo filtered2:", mcf_filtered2_mean_min_value)
max mean value in pseudo filtered2: 86.44239993023459 min mean value in pseudo filtered2: 11.483779541292405
sns.displot(pseudo_filtered2_summary.iloc[1], bins=100)
plt.xlim(0, 90)
plt.ylim(0, 30)
plt.title("Distribution of means in pseudo_filtered2")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
pseudo_filtered2_std_max_value = pseudo_filtered2_summary.iloc[2].max()
print("max std value in pseudo filtered2:", pseudo_filtered2_std_max_value)
mcf_filtered2_std_min_value = pseudo_filtered2_summary.iloc[2].min()
print("min std value in pseudo filtered2:", mcf_filtered2_std_min_value)
max std value in pseudo filtered2: 1668.9422310403038 min std value in pseudo filtered2: 66.39211740865552
sns.displot(pseudo_filtered2_summary.iloc[2], bins=100)
plt.xlim(0, 1700)
plt.ylim(0, 20)
plt.title("Distribution of STD on pseudo_filtered2")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
# similarity between filtered and pseudo_filtered2 data
# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set2 = set(pseudo_filtered2.columns)
unfiltered_set = set(df_MCF7_unf.columns)
# Calculate the intersection and union of the sets
intersection2 = filtered_set.intersection(pseudo_set2)
union2 = filtered_set.union(pseudo_set2)
# Calculate the Jaccard similarity coefficient
similarity2 = len(intersection2) / len(union2)
# Convert the similarity to percentage
similarity_percentage2 = similarity2 * 100
rem2=unfiltered_set - pseudo_set2
diff_2 = pseudo_set2 - filtered_set #elements in pseudo_filtered3 that are not in filtered
diff1_2 = filtered_set - pseudo_set2 # in filtered that are not in pseudo_filtered3
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage2}%", "\nnumber of removed", len(rem2),
"\nnumber of elements in pseudo_filtered2 that are not in filtered:", len(diff_2),
"\nnumber of elemtns in filtered that are not in pseudo_filtered2:", len(diff1_2))
# I removed 66 elements. 7 of these are mistakes. still need to remove 11 elements
Similarity Percentage: 97.8125% number of removed 63 number of elements in pseudo_filtered2 that are not in filtered: 7 number of elemtns in filtered that are not in pseudo_filtered2: 0
Check wheter mean=88 is bood upper bound
# check 1: reduce mean to 85
# any value of mean above 88 gives same result no other check needed
# Filter columns based on mean and standard deviation
filtered_columns4 = pseudo_filtered2.columns[(pseudo_filtered2.mean() < 85)]
# Create pseudo_filtered dataset
pseudo_filtered4 = df_MCF7_unf[filtered_columns4]
pseudo_filtered4_summary=pseudo_filtered4.describe()
# similarity between filtered and pseudo_filtered4 data
# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set4 = set(pseudo_filtered4.columns)
unfiltered_set = set(df_MCF7_unf.columns)
# Calculate the intersection and union of the sets
intersection4 = filtered_set.intersection(pseudo_set4)
union4 = filtered_set.union(pseudo_set4)
# Calculate the Jaccard similarity coefficient
similarity4 = len(intersection4) / len(union4)
# Convert the similarity to percentage
similarity_percentage4 = similarity4 * 100
rem4=unfiltered_set - pseudo_set4
diff_4 = pseudo_set4 - filtered_set #elements in pseudo_filtered3 that are not in filtered
diff1_4 = filtered_set - pseudo_set4 #elements in filtered that are not in pseudo_filtered3
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage4}%", "\nnumber of removed", len(rem4),
"\nnumber of elements in pseudo_filtered2 that are not in filtered:", len(diff_4),
"\nnumber of elemtns in filtered that are not in pseudo_filtered2:", len(diff1_4))
Similarity Percentage: 97.1875% number of removed 65 number of elements in pseudo_filtered2 that are not in filtered: 7 number of elemtns in filtered that are not in pseudo_filtered2: 2
¶
FILTERING MANTAINING CELLS WITH MEAN OVER GENES BETWEEN 11 AND 88 AND STD OVER 60 WE REACH A SIMILIARITY PERCENTAGE WITH FILTERED DATAFRAME OF 97.8125%:
- 62 REMOVED ELEMENTS
- 8 ELEMENTS WRONGLY FILTERED
- 0 MISSING FILTERS
¶
2.1.4) TRIAL AND ERROR OF PSEUDO FILTERING ON GENES
We follow same approach as before applied on genes
# similarity between filtered_transposed and unfiltred_transposed data
filtered_transposed_set=set(mcf_filtered_transposed.columns)
unfiltered_transposed_set=set(df_MCF7_unf_transposed.columns)
intersection=filtered_transposed_set.intersection(unfiltered_transposed_set)
union=filtered_transposed_set.union(unfiltered_transposed_set)
similarity_transposed=len(intersection)/len(union)
removed_transposed=unfiltered_transposed_set - filtered_transposed_set
print(f"Similarity Percentage: {similarity_transposed}%", "\nnumber of removed:", len(removed_transposed))
Similarity Percentage: 0.8260661027295718% number of removed: 3989
#trial 1: filter genes with mean below 0.1
filtered_transposed_columns = df_MCF7_unf_transposed.columns[(df_MCF7_unf_transposed.mean() >= 0.1)]
pseudo_transposed_filtered = df_MCF7_unf_transposed[filtered_transposed_columns]
pseudo_transposed_filtered_summary=pseudo_transposed_filtered.describe()
pseudo_transposed_filtered.describe()
| "WASH7P" | "WASH9P" | "MTND2P28" | "MTCO1P12" | "MTCO2P12" | "MTATP6P1" | "MTCO3P12" | "LINC01409" | "FAM87B" | "LINC01128" | ... | "MT-TH" | "MT-TS2" | "MT-TL2" | "MT-ND5" | "MT-ND6" | "MT-TE" | "MT-CYB" | "MT-TT" | "MT-TP" | "MAFIP" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | ... | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.000000 | 383.00000 | 383.000000 | 383.000000 | 383.000000 |
| mean | 0.133159 | 1.344648 | 6.261097 | 4.681462 | 0.524804 | 222.054830 | 0.433420 | 0.174935 | 0.302872 | 11.872063 | ... | 1.060052 | 0.443864 | 3.146214 | 1016.477807 | 204.600522 | 5.049608 | 2374.97389 | 2.083551 | 5.626632 | 1.749347 |
| std | 0.618664 | 2.244543 | 7.565749 | 6.232649 | 0.980857 | 262.616874 | 0.970848 | 0.711314 | 1.418711 | 18.673043 | ... | 1.990566 | 1.090827 | 4.265352 | 1009.444811 | 220.781927 | 6.644302 | 2920.39000 | 3.372714 | 7.511180 | 3.895204 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 23.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 172.000000 | 30.500000 | 0.000000 | 216.50000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 3.000000 | 2.000000 | 0.000000 | 98.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | ... | 0.000000 | 0.000000 | 1.000000 | 837.000000 | 152.000000 | 3.000000 | 785.00000 | 0.000000 | 3.000000 | 0.000000 |
| 75% | 0.000000 | 2.000000 | 10.000000 | 7.000000 | 1.000000 | 370.500000 | 1.000000 | 0.000000 | 0.000000 | 18.000000 | ... | 1.000000 | 0.000000 | 5.000000 | 1549.000000 | 294.000000 | 7.000000 | 4059.00000 | 3.000000 | 8.000000 | 2.000000 |
| max | 9.000000 | 20.000000 | 45.000000 | 36.000000 | 6.000000 | 1662.000000 | 10.000000 | 6.000000 | 17.000000 | 153.000000 | ... | 15.000000 | 8.000000 | 22.000000 | 8115.000000 | 2002.000000 | 46.000000 | 16026.00000 | 22.000000 | 56.000000 | 32.000000 |
8 rows × 17764 columns
pseudo_filtered_transposed_mean_max_value = pseudo_transposed_filtered_summary.iloc[1].max()
print("max mean value in pseudo filtered:", pseudo_filtered_transposed_mean_max_value)
mcf_filtered_transposed_mean_min_value = pseudo_transposed_filtered_summary.iloc[1].min()
print("min mean value in pseudo filtered:", mcf_filtered_transposed_mean_min_value)
max mean value in pseudo filtered: 23052.762402088774 min mean value in pseudo filtered: 0.10182767624020887
sns.displot(pseudo_transposed_filtered_summary.iloc[1], bins=500)
plt.xlim(0, 25000)
plt.ylim(0, 1000)
plt.title("Distribution of means over pseudo_transposed_filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
pseudo_filtered_transposed_std_max_value = pseudo_transposed_filtered_summary.iloc[2].max()
print("max std value in pseudo filtered:", pseudo_filtered_transposed_std_max_value)
mcf_filtered_transposed_mean_min_value = pseudo_transposed_filtered_summary.iloc[2].min()
print("min std value in pseudo filtered:", mcf_filtered_transposed_std_min_value)
max std value in pseudo filtered: 27101.548019606067 min std value in pseudo filtered: 0.13733946369142574
sns.displot(pseudo_transposed_filtered_summary.iloc[2], bins=500)
plt.xlim(0, 27200)
plt.ylim(0, 1000)
plt.title("Distribution of STD over pseudo_transposed_filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
# similarity between filtered_transposed and pseudo_transposed_filtered data
filtered_transposed_set=set(mcf_filtered_transposed.columns)
pseudo_transposed_filtered_set=set(pseudo_transposed_filtered.columns)
unfiltered_transposed_set=set(df_MCF7_unf_transposed.columns)
intersection=filtered_transposed_set.intersection(pseudo_transposed_filtered_set)
union=filtered_transposed_set.union(pseudo_transposed_filtered)
similarity_transposed=len(intersection)/len(union)
similarity_percentage_transposed=similarity_transposed*100
removed=unfiltered_transposed_set - pseudo_transposed_filtered_set
diff_transposed=pseudo_transposed_filtered_set-filtered_transposed_set
diff1_transposed=filtered_transposed_set-pseudo_transposed_filtered_set
print(f"Similarity Percentage: {similarity_percentage_transposed}%", "\nnumber of removed:", len(removed),
"\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed),
"\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed))
Similarity Percentage: 88.657621543838% number of removed: 5170 number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 513 number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 1694
Check whether mean=0.1 is good lowe bound
Note: with a lower threshold we would go back to original dataset with similarity 85%
# check 2 higher thresholds (mean=0.5. std=1)
filtered_transposed_columns_check = df_MCF7_unf_transposed.columns[(df_MCF7_unf_transposed.mean() >= 0.5) & (df_MCF7_unf_transposed.std() >= 0.1)]
pseudo_transposed_filtered_check = df_MCF7_unf_transposed[filtered_transposed_columns_check]
pseudo_transposed_filtered_check_summary=pseudo_transposed_filtered_check.describe()
# similarity between filtered_transposed and pseudo_transposed_filtered data
filtered_transposed_set=set(mcf_filtered_transposed.columns)
pseudo_transposed_filtered_check_set=set(pseudo_transposed_filtered_check.columns)
unfiltered_transposed_set=set(df_MCF7_unf_transposed.columns)
intersection_check=filtered_transposed_set.intersection(pseudo_transposed_filtered_check_set)
union_check=filtered_transposed_set.union(pseudo_transposed_filtered_check)
similarity_transposed_check=len(intersection_check)/len(union_check)
similarity_percentage_transposed_check=similarity_transposed_check*100
removed_check=unfiltered_transposed_set - pseudo_transposed_filtered_check_set
diff_transposed_check=pseudo_transposed_filtered_check_set-filtered_transposed_set
diff1_transposed_check=filtered_transposed_set-pseudo_transposed_filtered_check_set
print(f"Similarity Percentage: {similarity_percentage_transposed_check}%", "\nnumber of removed:", len(removed_check),
"\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed_check),
"\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed_check))
Similarity Percentage: 78.36997205967631% number of removed: 8044 number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 24 number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 4079
mean=0.1, std=0 is best lower bound
Furtermre, best option is to put no upper bound on mean and std since smiliarity will decrease. Infact:
# put upper bound mean=26900, std=27100 (just 1 element is removed)
filtered_transposed_column_check2 = pseudo_transposed_filtered.columns[(pseudo_transposed_filtered.mean() <= 26900) & (pseudo_transposed_filtered.std() <= 27100)]
pseudo_transposed_filtered_check2 = pseudo_transposed_filtered[filtered_transposed_column_check2]
pseudo_transposed_filtered_check2_summary=pseudo_transposed_filtered_check2.describe()
#similarity between filtered_transposed and pseudo_transposed_filtered data
filtered_transposed_set=set(mcf_filtered_transposed.columns)
pseudo_transposed_filtered_set_check2=set(pseudo_transposed_filtered_check2.columns)
unfiltered_transposed_set=set(df_MCF7_unf_transposed.columns)
intersection_check2=filtered_transposed_set.intersection(pseudo_transposed_filtered_set_check2)
union_check2=filtered_transposed_set.union(pseudo_transposed_filtered_check2)
similarity_transposed_check2=len(intersection_check2)/len(union_check2)
similarity_percentage_transposed_check2=similarity_transposed_check2*100
removed_check2=unfiltered_transposed_set - pseudo_transposed_filtered_set_check2
diff_transposed_check2=pseudo_transposed_filtered_set_check2-filtered_transposed_set
diff1_transposed_check2=filtered_transposed_set-pseudo_transposed_filtered_set_check2
print(f"Similarity Percentage: {similarity_percentage_transposed_check2}%", "\nnumber of removed:", len(removed_check2),
"\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed_check2),
"\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed_check2))
Similarity Percentage: 88.65248226950354% number of removed: 5171 number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 513 number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 1695
On genes the only filter is mean grater than 0.1 and we will reach Similarity Percentage: 88.657621543838%
¶
2.1.4bis) Merging the two approaches
Intersection based on cell (pseudo_filtered4 intersects pseudo_transposed_filtered transposed)
intersection_cell = pseudo_filtered.columns.intersection(pseudo_transposed_filtered.T.columns)
intersection_cell_set=set(intersection_cell)
filtered_set=set(mcf_filtered.columns)
unfiltered_set=set(df_MCF7_unf.columns)
int=intersection_cell_set.intersection(filtered_set)
un=intersection_cell_set.union(filtered_set)
similarity_cell = len(int)/len(un)
similarity_cell_percentage = similarity_cell*100
removed_cell = unfiltered_set - intersection_cell_set
diff_cell = filtered_set - intersection_cell_set
diff1_cell = intersection_cell_set - filtered_set
print(f"Similarity Percentage: {similarity_cell_percentage}%", "\nnumber of removed", len(removed_cell),
"\nnumber of elements in cell intersection that are not in filtered:", len(diff_cell),
"\nnumber of elemtns in filtered that are not in cell intersection:", len(diff1_cell))
Similarity Percentage: 96.60493827160494% number of removed 59 number of elements in cell intersection that are not in filtered: 0 number of elemtns in filtered that are not in cell intersection: 11
intersection based on genes (pseudo_filtered4 transposef intersects pseudo_transposed_filtered)
genes_pseudo4=pseudo_filtered2.T
intersection_genes= genes_pseudo4.columns.intersection(pseudo_transposed_filtered.columns)
intersection_genes_set=set(intersection_genes)
filtered_set_transposed=set(mcf_filtered_transposed.columns)
unfiltered_set_transposed=set(df_MCF7_unf_transposed.columns)
int_genes=intersection_genes_set.intersection(filtered_set_transposed)
un_genes=intersection_genes_set.union(filtered_set_transposed)
similarity_genes = len(int_genes)/len(un_genes)
similarity_genes_percentage = similarity_genes*100
removed_genes = unfiltered_set_transposed - intersection_genes_set
diff_genes = filtered_set_transposed - intersection_genes_set
diff1_genes = intersection_genes_set - filtered_set_transposed
print(f"Similarity Percentage: {similarity_genes_percentage}%", "\nnumber of removed", len(removed_genes),
"\nnumber of elements in genes intersection that are not in filtered:", len(diff_genes),
"\nnumber of elemtns in filtered that are not in genes intersection:", len(diff1_genes))
Similarity Percentage: 88.657621543838% number of removed 5170 number of elements in genes intersection that are not in filtered: 1694 number of elemtns in filtered that are not in genes intersection: 513
RESULT: best result is obtaining by filtering only on cells with mean between 11 and 88 and std over 60
¶
2.1.5) PCA
df_original = pd.read_csv("SmartSeq/MCF7_SmartS_Unfiltered_Data.txt",delimiter="\ ",engine='python',index_col=0)
df_filtered = pd.read_csv("SmartSeq/MCF7_SmartS_Filtered_Data.txt",delimiter="\ ",engine='python',index_col=0)
# PCA to reduce dimensionality
pca = PCA(n_components=50) # Reducing to 50 principal components
data_filtered_reduced = pca.fit_transform(df_filtered.T)
data_original_reduced = pca.fit_transform(df_original.T)
def extract_condition(name):
match = re.search(r'(Norm|Hypo)', name)
if match:
return match.group(1)
return 'Unknown'
conditions = df_filtered.T.index.to_series().apply(extract_condition)
conditions_original = df_original.T.index.to_series().apply(extract_condition)
plt.figure(figsize=(12, 6))
# Assign colors to conditions manually
color_map = {'Hypo': 'red', 'Norm': 'blue', 'Unknown': 'green'}
colors = conditions.map(color_map)
plt.scatter(data_filtered_reduced[:, 0], data_filtered_reduced[:, 1], c=colors)
plt.title('PCA of Cells Colored by Condition')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i],
label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')
plt.show()
From this PCA it is possible to obvserve that the data has been filtered in such a way that it is more easily separable. We can suppose the least expressive genes have been cut out, and looking at the PCA we can see how the two conditions appear to be almost linearly separable already in twpo dimension.
¶
2.1.6) Clustering
¶
2.1.7.1) Single linkage
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_filtered_reduced, 'single')
# Increase the figure size
plt.figure(figsize=(25, 10)) # You can adjust the size as necessary.
# Plotting a truncated dendrogram
dendrogram(
Z,
truncate_mode='lastp', # show only the last p merged clusters
p=50, # show only the last 50 merged clusters
leaf_rotation=90.,
leaf_font_size=12.,
show_contracted=True, # to get a sense of the dataset size if truncated
)
plt.title('Hierarchical Clustering Dendrogram (Single-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_original_reduced, 'single')
# Increase the figure size
plt.figure(figsize=(25, 10)) # You can adjust the size as necessary.
# Plotting a truncated dendrogram
dendrogram(
Z,
truncate_mode='lastp', # show only the last p merged clusters
p=50, # show only the last 50 merged clusters
leaf_rotation=90.,
leaf_font_size=12.,
show_contracted=True, # to get a sense of the dataset size if truncated
)
plt.title('Hierarchical Clustering Dendrogram (Single-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
¶
2.1.7.2) Average Linkage
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_filtered_reduced, 'average')
# Increase the figure size
plt.figure(figsize=(25, 10)) # You can adjust the size as necessary.
# Plotting a truncated dendrogram
dendrogram(
Z,
truncate_mode='lastp', # show only the last p merged clusters
p=50, # show only the last 50 merged clusters
leaf_rotation=90.,
leaf_font_size=12.,
show_contracted=True, # to get a sense of the dataset size if truncated
)
plt.title('Hierarchical Clustering Dendrogram (Average-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_original_reduced, 'average')
# Increase the figure size
plt.figure(figsize=(25, 10)) # You can adjust the size as necessary.
# Plotting a truncated dendrogram
dendrogram(
Z,
truncate_mode='lastp', # show only the last p merged clusters
p=50, # show only the last 50 merged clusters
leaf_rotation=90.,
leaf_font_size=12.,
show_contracted=True, # to get a sense of the dataset size if truncated
)
plt.title('Hierarchical Clustering Dendrogram (-Average-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
¶
2.1.7.3) Complete Linkage
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_filtered_reduced, 'complete')
# Increase the figure size
plt.figure(figsize=(25, 10)) # You can adjust the size as necessary.
# Plotting a truncated dendrogram
dendrogram(
Z,
truncate_mode='lastp', # show only the last p merged clusters
p=50, # show only the last 50 merged clusters
leaf_rotation=90.,
leaf_font_size=12.,
show_contracted=True, # to get a sense of the dataset size if truncated
)
plt.title('Hierarchical Clustering Dendrogram (Complete-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_original_reduced, 'complete')
# Increase the figure size
plt.figure(figsize=(25, 10)) # You can adjust the size as necessary.
# Plotting a truncated dendrogram
dendrogram(
Z,
truncate_mode='lastp', # show only the last p merged clusters
p=50, # show only the last 50 merged clusters
leaf_rotation=90.,
leaf_font_size=12.,
show_contracted=True, # to get a sense of the dataset size if truncated
)
plt.title('Hierarchical Clustering Dendrogram (Complete-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
Observing the hierarchical custering dendrograms of both the filtered and the unfiltered data we can make the following observations:
- the distance between clusters is generally lower in the filtered data
- in particular in the average-linkage and complete-linkage cases, the number of clusters is reduced This again suggests that the least expressive genes have been cut out during the filtering process, in order to make the remaining data more relevant with respect to the goal of the study.
¶
2.1.7) Unsupervised learning applied to MCF7
# Function to extract 'Normoxia' or 'Hypoxia' from a cell name
def extract_condition(name):
match = re.search(r'(Norm|Hypo)', name)
if match:
return match.group(1)
return 'Unknown'
df_MCF7_norm_filt = pd.read_csv("SmartSeq/MCF7_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)
df_MCF7_norm_filt_transpose = df_MCF7_norm_filt.T
# We transpose the original dataset so we make the cells to be the rows since they represents the observations
# and the genes to be columns since they are the features
# Apply the extract_condition function to each cell name in the DataFrame's index
conditions = df_MCF7_norm_filt_transpose.index.to_series().apply(extract_condition)
# We apply the pca to our dataset
# We basically reduce the dimensionality of our dataset to 2 dimensions
# because we want to see the data in a 2D space
pca = PCA(n_components=2)
principal_components = pca.fit_transform(df_MCF7_norm_filt_transpose)
# now based on what we have reduced we apply the k-means to try to clusters cells in two groups
# The groups should be based on the cell with Normoxia and Hypoxia
# Please notice that this k-means should use the so called k++ to initialize the centroids
# If you don't know what k++ is, please add init keywords and set it to something different
k = 2
kmeans = KMeans(n_clusters=k, init='k-means++', random_state=42)
clusters = kmeans.fit_predict(principal_components)
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1. warnings.warn(
# Assign numeric labels based on conditions
condition_numeric = conditions.map({'Norm': 0, 'Hypo': 1})
plt.figure(figsize=(12, 6))
# Assign colors to conditions manually
color_map = {'Hypo': 'red', 'Norm': 'blue', 'Unknown': 'green'}
colors = conditions.map(color_map)
plt.scatter(principal_components[:, 0], principal_components[:, 1], c=colors)
plt.title('PCA of Cells Colored by Condition')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i],
label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')
plt.show()
# Visualization of PCA with K-means clusters, with a proper legend
plt.figure(figsize=(12, 6))
# Assign colors based on the K-means cluster result
# Here we assign 'red' to cluster 1 and 'blue' to cluster 0
cluster_colors = ['green' if label == 0 else 'yellow' for label in clusters]
# Scatter plot with assigned colors
scatter = plt.scatter(principal_components[:, 0], principal_components[:, 1], c=cluster_colors)
# Create a legend for the colors
plt.title('PCA of Cells Colored by K-means Clusters')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
# Assuming that after any potential inversion, cluster 0 corresponds to Normoxia and cluster 1 to Hypoxia
legend_labels = {0: 'Cluster1', 1: 'Cluster2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Condition')
plt.show()
Here the clusters given by k-means are remarkably similar to the actual suddivision of cells in hypoxic and normoxic: this shows that, along the two principal component given by the PCA, the cells are extremely separated in the two groups.
¶
2.1.8) Looking for most useful and expressed genes across cells
#I want to split the dataset in 2 parts, one with RNA with hypoxia in the name and the other with RNA with normoxia in the name
hypoxia_samples = df_MCF7_norm_filt.T[df_MCF7_norm_filt.T.index.str.contains('Hypo')]
normoxia_samples = df_MCF7_norm_filt.T[df_MCF7_norm_filt.T.index.str.contains('Norm')]
hypoxia_samples
| "CYP1B1" | "CYP1B1-AS1" | "CYP1A1" | "NDRG1" | "DDIT4" | "PFKFB3" | "HK2" | "AREG" | "MYBL2" | "ADM" | ... | "CD27-AS1" | "DNAI7" | "MAFG" | "LZTR1" | "BCO2" | "GRIK5" | "SLC25A27" | "DENND5A" | "CDK5R1" | "FAM13A-AS1" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "output.STAR.2_B7_Hypo_S79_Aligned.sortedByCoord.out.bam" | 5817 | 2669 | 0 | 654 | 2484 | 1603 | 748 | 436 | 0 | 0 | ... | 0 | 0 | 63 | 0 | 0 | 0 | 0 | 60 | 0 | 0 |
| "output.STAR.2_B9_Hypo_S81_Aligned.sortedByCoord.out.bam" | 3841 | 1565 | 79 | 1263 | 2596 | 2795 | 3970 | 373 | 554 | 31 | ... | 0 | 0 | 102 | 42 | 0 | 0 | 0 | 107 | 0 | 0 |
| "output.STAR.2_C10_Hypo_S130_Aligned.sortedByCoord.out.bam" | 9263 | 3866 | 238 | 2634 | 1323 | 2624 | 1265 | 646 | 0 | 0 | ... | 0 | 0 | 92 | 0 | 0 | 0 | 0 | 74 | 0 | 0 |
| "output.STAR.2_C11_Hypo_S131_Aligned.sortedByCoord.out.bam" | 21543 | 9113 | 443 | 540 | 2044 | 3449 | 2034 | 0 | 0 | 2170 | ... | 4 | 0 | 120 | 56 | 0 | 0 | 0 | 127 | 0 | 13 |
| "output.STAR.2_C8_Hypo_S128_Aligned.sortedByCoord.out.bam" | 1338 | 554 | 14 | 634 | 3513 | 1360 | 303 | 558 | 178 | 994 | ... | 0 | 0 | 46 | 5 | 0 | 0 | 0 | 14 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| "output.STAR.4_H10_Hypo_S382_Aligned.sortedByCoord.out.bam" | 7890 | 3647 | 86 | 481 | 3692 | 4702 | 1011 | 2707 | 144 | 385 | ... | 0 | 0 | 0 | 20 | 0 | 0 | 0 | 179 | 0 | 0 |
| "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" | 4512 | 2035 | 1654 | 1052 | 2410 | 2561 | 999 | 717 | 175 | 612 | ... | 0 | 0 | 62 | 22 | 0 | 0 | 0 | 97 | 0 | 0 |
| "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" | 34565 | 13717 | 11274 | 1263 | 4256 | 5663 | 701 | 0 | 0 | 0 | ... | 0 | 0 | 45 | 0 | 0 | 0 | 0 | 140 | 0 | 32 |
| "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" | 20024 | 7835 | 563 | 925 | 12733 | 2187 | 1141 | 0 | 20 | 81 | ... | 0 | 0 | 57 | 8 | 0 | 0 | 0 | 7 | 0 | 11 |
| "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" | 5953 | 2367 | 522 | 1572 | 2275 | 3269 | 2247 | 169 | 2 | 0 | ... | 0 | 0 | 165 | 0 | 0 | 0 | 0 | 37 | 0 | 12 |
124 rows × 3000 columns
hypoxia_summary = hypoxia_samples.describe()
normoxia_summary = normoxia_samples.describe()
normoxia_summary
| "CYP1B1" | "CYP1B1-AS1" | "CYP1A1" | "NDRG1" | "DDIT4" | "PFKFB3" | "HK2" | "AREG" | "MYBL2" | "ADM" | ... | "CD27-AS1" | "DNAI7" | "MAFG" | "LZTR1" | "BCO2" | "GRIK5" | "SLC25A27" | "DENND5A" | "CDK5R1" | "FAM13A-AS1" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 126.000000 | 126.000000 | 126.000000 | 126.000000 | 126.000000 | 126.000000 | 126.000000 | 126.000000 | 126.000000 | 126.000000 | ... | 126.000000 | 126.000000 | 126.000000 | 126.000000 | 126.000000 | 126.000000 | 126.000000 | 126.000000 | 126.000000 | 126.000000 |
| mean | 296.079365 | 121.126984 | 1.111111 | 18.603175 | 344.960317 | 157.769841 | 28.817460 | 112.238095 | 648.317460 | 1.746032 | ... | 26.507937 | 0.111111 | 27.436508 | 28.849206 | 0.079365 | 0.031746 | 0.071429 | 30.119048 | 4.206349 | 0.388889 |
| std | 301.404382 | 124.625325 | 7.706851 | 44.026734 | 620.660453 | 289.259279 | 53.607671 | 246.050968 | 582.416478 | 6.318147 | ... | 29.943613 | 1.247219 | 25.844921 | 37.043772 | 0.601374 | 0.356348 | 0.801784 | 29.118958 | 9.571054 | 2.342553 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 36.500000 | 14.000000 | 0.000000 | 0.000000 | 22.000000 | 24.250000 | 0.000000 | 0.000000 | 144.000000 | 0.000000 | ... | 1.000000 | 0.000000 | 4.250000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 |
| 50% | 228.000000 | 99.000000 | 0.000000 | 1.000000 | 97.000000 | 71.000000 | 8.500000 | 2.000000 | 555.000000 | 0.000000 | ... | 18.500000 | 0.000000 | 22.500000 | 15.000000 | 0.000000 | 0.000000 | 0.000000 | 23.500000 | 0.000000 | 0.000000 |
| 75% | 422.250000 | 175.000000 | 0.000000 | 16.250000 | 317.250000 | 156.500000 | 34.500000 | 73.750000 | 966.750000 | 0.000000 | ... | 39.000000 | 0.000000 | 41.750000 | 45.250000 | 0.000000 | 0.000000 | 0.000000 | 51.000000 | 0.000000 | 0.000000 |
| max | 1522.000000 | 596.000000 | 78.000000 | 296.000000 | 3506.000000 | 2111.000000 | 284.000000 | 1673.000000 | 3108.000000 | 41.000000 | ... | 165.000000 | 14.000000 | 125.000000 | 196.000000 | 6.000000 | 4.000000 | 9.000000 | 115.000000 | 55.000000 | 18.000000 |
8 rows × 3000 columns
# I want to do the mean of the mean in the hypoxia samples and return the value
hypoxia_mean = hypoxia_samples.describe().loc['mean'].mean()
hypoxia_mean
139.35825268817206
# Now let's dothe same with the standard deviation
hypoxia_std = hypoxia_samples.describe().loc['std'].mean()
hypoxia_std
135.42901730349217
normoxia_samples
| "CYP1B1" | "CYP1B1-AS1" | "CYP1A1" | "NDRG1" | "DDIT4" | "PFKFB3" | "HK2" | "AREG" | "MYBL2" | "ADM" | ... | "CD27-AS1" | "DNAI7" | "MAFG" | "LZTR1" | "BCO2" | "GRIK5" | "SLC25A27" | "DENND5A" | "CDK5R1" | "FAM13A-AS1" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "output.STAR.2_B3_Norm_S57_Aligned.sortedByCoord.out.bam" | 343 | 140 | 0 | 0 | 386 | 75 | 0 | 0 | 476 | 0 | ... | 63 | 0 | 17 | 59 | 0 | 0 | 0 | 51 | 0 | 0 |
| "output.STAR.2_B4_Norm_S58_Aligned.sortedByCoord.out.bam" | 131 | 59 | 0 | 1 | 289 | 42 | 0 | 856 | 1586 | 0 | ... | 0 | 0 | 1 | 25 | 0 | 0 | 0 | 34 | 0 | 0 |
| "output.STAR.2_B5_Norm_S59_Aligned.sortedByCoord.out.bam" | 452 | 203 | 0 | 0 | 0 | 0 | 0 | 0 | 775 | 0 | ... | 15 | 0 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "output.STAR.2_B6_Norm_S60_Aligned.sortedByCoord.out.bam" | 27 | 7 | 0 | 0 | 288 | 214 | 15 | 242 | 1191 | 0 | ... | 1 | 0 | 3 | 10 | 0 | 0 | 0 | 4 | 0 | 0 |
| "output.STAR.2_C1_Norm_S103_Aligned.sortedByCoord.out.bam" | 1013 | 459 | 0 | 0 | 36 | 75 | 15 | 6 | 1120 | 0 | ... | 0 | 0 | 80 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" | 160 | 75 | 0 | 0 | 800 | 1041 | 39 | 0 | 0 | 0 | ... | 35 | 0 | 0 | 35 | 0 | 0 | 0 | 0 | 26 | 0 |
| "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" | 351 | 138 | 0 | 0 | 1 | 57 | 26 | 0 | 331 | 0 | ... | 10 | 0 | 12 | 2 | 0 | 0 | 0 | 35 | 0 | 0 |
| "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" | 327 | 130 | 0 | 54 | 189 | 235 | 232 | 1 | 1 | 0 | ... | 13 | 0 | 26 | 0 | 0 | 0 | 0 | 56 | 0 | 0 |
| "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" | 196 | 102 | 1 | 243 | 266 | 278 | 78 | 1 | 199 | 0 | ... | 79 | 0 | 1 | 0 | 0 | 0 | 0 | 45 | 19 | 0 |
| "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" | 504 | 238 | 0 | 62 | 417 | 932 | 275 | 0 | 33 | 0 | ... | 86 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
126 rows × 3000 columns
normoxia_mean = normoxia_samples.describe().loc['mean'].mean()
normoxia_mean
92.81419841269842
normoxia_std = normoxia_samples.describe().loc['std'].mean()
normoxia_std
75.52590828017712
#we look at how many genes have a mean of expression greater than 1 and we print their names
#for hypoxia samples
hypoxia_samples_summary = hypoxia_samples.describe()
hypo_genes = hypoxia_samples_summary.loc['mean'][hypoxia_samples_summary.loc['mean'] > 139.358]
hypo_genes_sorted = sorted(hypo_genes.items(), key=lambda x: x[1], reverse=True)
print(hypo_genes_sorted)
print(len(hypo_genes_sorted)) # 384 genes overcome the mean threshold
## Now we apply the sqame with the standard deviation
hypo_genes_std = hypoxia_samples_summary.loc['std'][hypoxia_samples_summary.loc['std'] > 135.429]
hypo_genes_std_sorted = sorted(hypo_genes_std.items(), key=lambda x: x[1], reverse=True)
print(hypo_genes_std_sorted)
print(len(hypo_genes_std_sorted)) # 520 genes overcome the mean std threshold
[('"GAPDH"', 46292.032258064515), ('"ACTG1"', 26915.354838709678), ('"ALDOA"', 26235.370967741936), ('"FTH1"', 10966.370967741936), ('"CYP1B1"', 10696.193548387097), ('"BEST1"', 9709.016129032258), ('"PGK1"', 8696.25), ('"GPI"', 7138.314516129032), ('"UBC"', 6946.177419354839), ('"LDHA"', 5867.25), ('"FTL"', 4929.6612903225805), ('"DDIT4"', 4663.5887096774195), ('"CYP1B1-AS1"', 4430.491935483871), ('"MT-ND4"', 3267.1774193548385), ('"CYP1A1"', 3233.9112903225805), ('"TMSB10"', 2976.8709677419356), ('"PFKFB3"', 2855.6532258064517), ('"SLC3A2"', 2535.032258064516), ('"SLC2A1"', 2530.8709677419356), ('"MT-RNR2"', 2515.3951612903224), ('"MT-CO1"', 2497.6612903225805), ('"DSP"', 2441.1935483870966), ('"HSPB1"', 2355.532258064516), ('"NCOA3"', 2331.0887096774195), ('"KRT80"', 2217.8951612903224), ('"BNIP3"', 2209.5887096774195), ('"CTSD"', 2168.467741935484), ('"FLNA"', 2072.1209677419356), ('"HSPA5"', 2041.0887096774193), ('"SQSTM1"', 1990.008064516129), ('"PSAP"', 1985.8387096774193), ('"SULF2"', 1980.766129032258), ('"TMSB4X"', 1958.975806451613), ('"BHLHE40"', 1735.467741935484), ('"HK2"', 1721.5725806451612), ('"IDH2"', 1708.524193548387), ('"ANXA2"', 1690.5645161290322), ('"CLDN4"', 1613.3064516129032), ('"AARS1"', 1565.782258064516), ('"TFF1"', 1561.6693548387098), ('"PFKP"', 1549.1935483870968), ('"CDKN1A"', 1449.1532258064517), ('"MALAT1"', 1448.0), ('"SLC1A5"', 1400.7016129032259), ('"ATP1A1"', 1395.0806451612902), ('"STC2"', 1253.3387096774193), ('"TRIB3"', 1205.3064516129032), ('"NDRG1"', 1203.6370967741937), ('"VEGFA"', 1190.1048387096773), ('"JUP"', 1124.7903225806451), ('"GSN"', 1099.258064516129), ('"MYH9"', 1090.3145161290322), ('"DNAJB1"', 1081.3709677419354), ('"HSPA8"', 1065.975806451613), ('"MT-CO2"', 1055.1612903225807), ('"UBB"', 1022.266129032258), ('"S100A11"', 1018.6370967741935), ('"GPRC5A"', 1017.3145161290323), ('"SCD"', 1008.8709677419355), ('"SARS1"', 1006.8709677419355), ('"HILPDA"', 987.2096774193549), ('"ELF3"', 975.483870967742), 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('"UGDH"', 736.5020018493065), ('"DDIT3"', 733.7653971209489), ('"TACSTD2"', 732.5582519137247), ('"KRT86"', 723.8548467535695), ('"ELF3"', 721.5316081270157), ('"HSPA1A"', 718.0555307769083), ('"ZFP36L1"', 711.5149427585383), ('"MT-ND5"', 708.2639178124998), ('"SLC39A6"', 702.5408871645473), ('"NDRG1"', 688.8829102093435), ('"PTP4A1"', 683.0366179348008), ('"TFF3"', 678.9721584198116), ('"GATA3"', 671.0420151136079), ('"KLF10"', 666.0857353660391), ('"SARS1"', 656.5639303201401), ('"TRIM16"', 645.5988993897525), ('"ZYX"', 634.0774570545112), ('"TNFRSF12A"', 633.6646337636924), ('"ILVBL"', 629.5802057800872), ('"NQO1"', 628.0199888636299), ('"KLF6"', 627.9954084897323), ('"ADM"', 616.3903243852907), ('"DKK1"', 611.1376991907372), ('"TUFT1"', 602.3969563643053), ('"MT-CYB"', 584.4241859842803), ('"FXYD3"', 582.9358190166205), ('"ERO1A"', 581.2703132157739), ('"TPM4"', 578.9589521995683), ('"CDC20"', 563.9933932239936), ('"XPOT"', 563.4636594917142), ('"GRN"', 561.7635185033083), ('"KDM3A"', 560.2645741640325), ('"AHNAK"', 559.9582224216305), ('"COX6C"', 559.3287892342925), ('"TGIF1"', 558.2831128503614), ('"PHLDA1-AS1"', 551.1242375273828), ('"TSC22D2"', 550.3728610009852), ('"JAG1"', 548.2806238352025), ('"PLK1"', 542.9790856356373), ('"PFKP"', 538.7588518491768), ('"TUBA1B"', 532.2429410725251), ('"LMNA"', 531.9055442453586), ('"DUSP1"', 528.128642753465), ('"ACKR3"', 519.4928195859532), ('"AMIGO2"', 515.5698490759137), ('"SLCO4A1"', 515.2686992389708), ('"RBCK1"', 508.0786175926566), ('"NEDD9"', 506.1046242006622), ('"KDM5B"', 500.6026220944476), ('"ENO2"', 496.87129242415267), ('"CRIP2"', 490.59724964158517), ('"H2BC21"', 487.87007304139934), ('"SAT1"', 481.04018799939746), ('"PLK2"', 478.84708411275057), ('"FLNB"', 476.70643363156177), ('"PPM1D"', 471.58920900319157), ('"TUBB"', 465.28127207386063), ('"IGFBP2"', 462.901587405298), ('"CLK3"', 456.5393126348675), ('"ZMYND8"', 456.3378152343975), ('"PHLDA3"', 453.4656945274875), ('"DDR1"', 452.7206675415894), ('"CCNB1"', 449.5580465174275), ('"TFRC"', 448.5261419067722), ('"LMCD1"', 447.2368231304395), ('"MBTPS1"', 446.87013855448805), ('"WARS1"', 439.3622183688267), ('"IARS1"', 436.77104398035664), ('"OVOL1"', 434.1424935369336), ('"PIM3"', 433.0404183605848), ('"CEACAM6"', 430.8186197502675), ('"TRAFD1"', 429.751638992674), ('"IGFBP5"', 422.0514356946149), ('"TXNDC5"', 416.4134189999285), ('"ID1"', 415.7196834792655), ('"PFKFB4"', 414.77337071690306), ('"UCP2"', 414.6522343827726), ('"DUSP4"', 414.1468572791273), ('"MYBL2"', 408.99404995745715), ('"ITGA5"', 408.50200482876613), ('"ANGPTL4"', 407.65458461704685), ('"BLOC1S5-TXNDC5"', 407.2515255646617), ('"PPP1R15A"', 405.60605481436284), ('"PADI2"', 405.1760104724438), ('"CITED2"', 401.81546736505175), ('"CLU"', 401.1926969114403), ('"PTPRF"', 399.8345917508339), ('"PMEPA1"', 397.0554836635109), ('"SRF"', 395.9080738305379), ('"SPTSSB"', 393.2080963916251), ('"TPX2"', 392.33879041219336), ('"ID3"', 392.14091127530946), ('"GLUL"', 388.5952206036547), ('"SLC25A25"', 387.3290206783348), ('"ID2"', 386.67326009282095), ('"AGR2"', 384.5337682468324), ('"MYADM"', 383.2856342973135), ('"MYO1B"', 381.6994085663074), ('"SEMA3C"', 381.659605175079), ('"OSER1"', 378.5093888681186), ('"QSOX1"', 376.9489850186398), ('"GFRA1"', 376.52643263328105), ('"AURKA"', 375.02665313096776), ('"TSPAN15"', 372.7862025075008), ('"TXNRD1"', 371.3594887958286), ('"TSC22D3"', 364.98345744247285), ('"EEF1A2"', 364.7341918226209), ('"ISG15"', 363.41126416009314), ('"FAM162A"', 363.4027195624241), ('"TLE1"', 363.3579877684147), ('"PRC1"', 359.2458359030623), ('"FAM83D"', 358.505218382743), ('"HMGB2"', 357.31214246348947), ('"RAP2B"', 356.61921984780497), ('"KIF22"', 354.3246775455458), ('"HLA-C"', 352.61729898898744), ('"IER2"', 351.89162224756814), ('"DSCAM-AS1"', 349.7726438323318), ('"PHGDH"', 346.9029188833945), ('"SLC38A2"', 345.64333757633756), ('"TUBB4B"', 345.32913295986253), ('"DDX41"', 342.44834625551056), ('"CEACAM5"', 341.4937413006212), ('"IRF2BP2"', 341.0061033706887), ('"KYNU"', 338.31044033374815), ('"ST3GAL1"', 335.3551161979438), ('"PLIN2"', 334.82622865641173), ('"EGLN3"', 330.53130544051794), ('"ATP9A"', 329.66656497102554), ('"IGF1R"', 329.1834778113744), ('"BNIP3L"', 328.72743879868784), ('"TGFBI"', 327.71133784876724), ('"THBS1"', 327.68023180657525), ('"MT-CO3"', 327.50927458774555), ('"LOXL2"', 323.6836404098523), ('"HSPA1B"', 320.84941519839145), ('"AEN"', 320.322187333122), ('"KIF20A"', 318.08692867033386), ('"ASS1"', 316.53076369291153), ('"HIGD1A"', 315.8744476317555), ('"P4HA2"', 315.8603032715502), ('"CHPF"', 312.9376633088937), ('"JUN"', 311.7705286101218), ('"LY6E"', 311.4379022245694), ('"LPCAT1"', 310.47800933837004), ('"N4BP3"', 310.288158670231), ('"TANC2"', 307.3896638714814), ('"PRSS23"', 307.24290839664866), ('"BAG1"', 307.0299624620736), ('"NME1-NME2"', 306.98275523478435), ('"NOL3"', 305.40824294653623), ('"IER5L"', 305.255819484473), ('"CIART"', 304.5721908622486), ('"PRSS8"', 303.4937876633975), ('"DNAJA1"', 300.5226433498997), ('"ECE1"', 299.89720712492857), ('"ACAA2"', 294.89900601342356), ('"OSR2"', 294.4091474999629), ('"EIF4EBP1"', 293.5265266708076), ('"ITPRID2"', 293.4040155465511), ('"ALDOC"', 292.7033644707813), ('"SLC6A14"', 291.87387420732637), ('"ITGB4"', 290.74610245421303), ('"ARRDC3"', 288.5698235719006), ('"PAWR"', 288.53193710689686), ('"MYLIP"', 287.584596728291), ('"CCN5"', 287.51192223766327), ('"SREBF1"', 285.6686116198818), ('"CASP14"', 282.80524798425785), ('"SPTBN1"', 282.4034797014644), ('"ELAPOR1"', 281.6142505042501), ('"EDN1"', 280.5724371229616), ('"GYS1"', 279.18780897038044), ('"ANXA1"', 278.8985608160823), ('"H4C3"', 278.22083142452203), ('"LYPD3"', 277.53451974699163), ('"TPM1"', 277.468656305718), ('"MT-ATP6"', 277.3689156020374), ('"TUBA1A"', 276.6207043887206), ('"TNFRSF21"', 276.32208045194733), ('"NR2F2"', 275.8480601851208), ('"H1-0"', 275.28549362958597), ('"EDN2"', 273.9091805357144), ('"CCN2"', 273.8412791342476), ('"SCARB1"', 273.22479454768643), ('"ANXA3"', 270.9696433187945), ('"SLC9A3R2"', 270.2382261249618), ('"SEMA4B"', 269.47270927460823), ('"PYGB"', 269.0631827812017), ('"ITGB5"', 267.9802152237677), ('"CD55"', 265.31715838193475), ('"LOX"', 264.10044002361326), ('"FOSL2"', 263.9170823023266), ('"FADS3"', 263.900974417399), ('"LAMB2"', 263.826454906286), ('"RASSF7"', 262.41156753522057), ('"IER5"', 261.63553183983254), ('"AMOTL2"', 260.98024027059375), ('"MKNK2"', 260.9285426446108), ('"DHCR7"', 260.6915458690502), ('"PCK2"', 260.5264385374851), ('"MAGED2"', 260.20144143698576), ('"MCM7"', 259.6448748854634), ('"CD59"', 259.2965892889638), ('"P4HA1"', 258.39044693221035), ('"WSB1"', 257.78081490161395), ('"DHCR24"', 257.3101864702774), ('"NFE2L2"', 256.96867783833426), ('"H2AZ1"', 256.1333709181131), ('"NR4A1"', 255.64687239907173), ('"DEGS2"', 255.21966236087542), ('"CACNG4"', 254.5928035258136), ('"GADD45A"', 254.54249479186174), ('"SLC9A3R1"', 253.2918655326897), ('"DLC1"', 249.9106280064106), ('"RAB31"', 248.1406265042037), ('"H1-2"', 247.980295825606), ('"GRAMD1A"', 246.39256540937114), ('"IFT122"', 246.34212356739272), ('"CKAP5"', 246.3277998900829), ('"PDLIM7"', 244.8273770064629), ('"SPOCK1"', 244.5468547644606), ('"BCAR1"', 243.91823029132132), ('"S100P"', 241.1060986368764), ('"LGALS1"', 240.2449907618318), ('"TOP2A"', 239.7893849223244), ('"ALCAM"', 238.79891179138804), ('"SYTL2"', 237.3509321409929), ('"LXN"', 236.37282357126574), ('"XPC"', 235.70159913940722), ('"CALM2"', 235.67518775925606), ('"NRP1"', 235.50420253688904), ('"PIM1"', 234.2736256066076), ('"NUPR1"', 233.47482582888918), ('"ETS2"', 233.15568342156172), ('"HMOX1"', 231.9665548299244), ('"MAGED1"', 231.2992323958241), ('"H2AC6"', 230.24456660492206), ('"RND3"', 230.08255211859216), ('"GSTM3"', 227.96567980084208), ('"GPX3"', 226.54951467395247), ('"PDLIM5"', 225.44320584956503), ('"SCUBE2"', 225.15960886083812), ('"SIPA1L2"', 223.6284505833434), ('"ADGRG1"', 223.48468670752735), ('"TES"', 222.4527295620088), ('"RGS16"', 221.6029916688878), ('"CCN1"', 220.31017818785736), ('"NCAPD2"', 220.1130308008827), ('"MT-RNR1"', 219.2764375055123), ('"VCL"', 218.25828444908626), ('"PLOD2"', 217.16119479920775), ('"DAAM1"', 216.98860994301967), ('"IRF9"', 215.59901412989916), ('"GADD45G"', 214.64796761208805), ('"PINK1"', 213.9003954259748), ('"OBSL1"', 213.4813134153677), ('"GRB10"', 213.3170182507099), ('"PLOD1"', 212.9340144402454), ('"PTGER4"', 212.14641955666067), ('"TOB1"', 210.65201503857296), ('"CXXC5"', 210.55788920141822), ('"NR3C1"', 210.4467361204999), ('"FZR1"', 209.75716402533854), ('"LAMA3"', 209.18720150636022), ('"TUBA1C"', 207.91060710825894), ('"TMEM45A"', 206.70593229729093), ('"AVPI1"', 205.89586000338997), ('"SH3BP4"', 205.785903757172), ('"PEBP1"', 205.35715850482026), ('"PDXK"', 205.00756176365297), ('"NME1"', 204.59518537037346), ('"BTG1"', 203.12493829812053), ('"LRP4"', 202.47735851380128), ('"PALLD"', 201.9132254940861), ('"DUSP2"', 201.253708226071), ('"SHB"', 201.10905455178235), ('"SMIM14"', 200.5681112985468), ('"FHL2"', 199.1303297124326), ('"NECTIN2"', 196.897046927846), ('"TSKU"', 195.83293079066496), ('"TUBA4A"', 194.46256441491522), ('"DEDD2"', 193.06531071483613), ('"ZNF292"', 192.693480785864), ('"HLA-B"', 192.6668166270877), ('"ID4"', 191.3423935059433), ('"PLAUR"', 191.27541938483495), ('"MCM2"', 191.27359167917723), ('"WEE1"', 190.88641490072914), ('"CSRNP1"', 190.68300275586907), ('"TBC1D9"', 190.5393362464173), ('"INSIG1"', 190.13937494378325), ('"IFI6"', 189.64283655077077), ('"CEBPG"', 189.5586802201031), ('"TUBB3"', 188.19777305635864), ('"MGLL"', 188.15524584278515), ('"GADD45B"', 188.1251311629715), ('"ARPC1B"', 186.9285301224013), ('"TAP1"', 186.16785147309554), ('"CHD2"', 186.02327400360468), ('"FBP1"', 184.92653381402852), ('"IFFO2"', 184.01324155024122), ('"PXDN"', 183.42598235925695), ('"ARFGEF3"', 183.08300746190392), ('"CDH3"', 182.898327825958), ('"ISOC1"', 182.68829351798374), ('"OAS1"', 182.49393952193162), ('"BCL6"', 181.6250946137221), ('"GTPBP2"', 181.12644300495018), ('"KIF2C"', 179.1111565091571), ('"BMP4"', 178.99003967734598), ('"ANLN"', 178.28407716028914), ('"FAM102A"', 178.2453018211015), ('"IFRD1"', 177.77363172262372), ('"AKAP12"', 177.01444727014544), ('"EPAS1"', 176.72698034971918), ('"KCTD11"', 176.0652004957159), ('"GBE1"', 176.06185043054037), ('"SLC25A39"', 175.38159886084873), ('"S100A16"', 175.19700705158226), ('"DYNC2I2"', 174.77434404084283), ('"MYC"', 174.40174067895344), ('"CYBA"', 174.15442167373465), ('"TSPYL2"', 173.8872750425264), ('"LDLR"', 172.929018150936), ('"IRF6"', 172.8335813048081), ('"DAPK3"', 172.55645795737607), ('"ANKZF1"', 172.43691876163382), ('"IFI27"', 172.13586751268022), ('"TRIB1"', 172.0411705722954), ('"RHOBTB3"', 171.11228355995834), ('"PYGL"', 170.5251148024528), ('"FUT11"', 169.45372865551917), ('"INHA"', 169.25193396782473), ('"SEMA3B"', 169.12819665958696), ('"INPP4B"', 169.0121145740848), ('"APBB2"', 168.75461101411744), ('"BMP7"', 168.6499988784138), ('"ATP1B1"', 168.54833055386248), ('"CCNB2"', 168.41498721988134), ('"STC1"', 168.27632664333962), ('"SERPINE1"', 168.07894845493402), ('"HNRNPA2B1"', 168.06707617151042), ('"STARD10"', 167.06422561771973), ('"FKBP4"', 166.81298586386885), ('"MCM3"', 166.04685589616838), ('"CAPN2"', 164.71849560902055), ('"PIK3IP1"', 164.56811050448692), ('"NOTCH1"', 164.55479737811373), ('"SLC12A2"', 164.4841515964673), ('"TMEM64"', 163.07971560000445), ('"TSPAN1"', 162.9741015097684), ('"HES1"', 162.9117970132441), ('"TK1"', 162.45209485729862), ('"ADAM15"', 162.4063188981375), ('"RACGAP1"', 162.0941442651013), ('"SPAG5"', 161.47903219604672), ('"ZWINT"', 160.958263987499), ('"SLC6A6"', 159.86634366354073), ('"CA12"', 159.68406779638013), ('"ERBB3"', 159.59957002170964), ('"TMEM106C"', 159.47469888043528), ('"GREB1"', 159.38349027075876), ('"ZNF592"', 158.9160806188448), ('"KLF3"', 158.8191797758491), ('"CABLES1"', 158.8180626564995), ('"CA9"', 158.60535386400014), ('"LGALS3BP"', 158.3982538375276), ('"TRIM16L"', 157.2147915742841), ('"ABCG2"', 156.65479361965856), ('"SAPCD2"', 156.4783150689203), ('"FOXO1"', 156.3876238913199), ('"NOTCH2"', 155.8369472997943), ('"EGR1"', 155.75627429217576), ('"OSGIN1"', 155.73093962762815), ('"CAV1"', 155.5975923321924), ('"PAM"', 154.5377774708101), ('"NECTIN4"', 154.483482210553), ('"LPP"', 154.19442084720149), ('"LAMC2"', 153.96381654682648), ('"VASN"', 153.55529501047508), ('"PTTG1"', 153.36198178189835), ('"PODXL"', 153.2636798887446), ('"NUP188"', 153.21313291135306), ('"ITPKC"', 153.19785810096803), ('"CA2"', 152.85483154918833), ('"SMTN"', 152.0946414680133), ('"GPAA1"', 151.53899781888748), ('"CRABP2"', 151.18127138027137), ('"NINJ1"', 151.02593183819482), ('"NUSAP1"', 150.99904799938895), ('"SERPINA3"', 150.7176425071609), ('"SLC25A29"', 150.71717420752512), ('"PRMT6"', 149.02805023249), ('"DSG2"', 148.88875620090562), ('"PIEZO1"', 148.776401082342), ('"RRM2"', 148.77108707450927), ('"STMN1"', 148.35917849316948), ('"ELP2"', 148.07171712387057), ('"SOX9"', 146.53880295350592), ('"GOLM1"', 145.298293923748), ('"PDK1"', 144.08892877031818), ('"UBE2S"', 143.9138240827807), ('"SNAI2"', 143.7805471026969), ('"ST6GALNAC2"', 143.3648600047643), ('"TROAP"', 142.70379807047829), ('"H3C4"', 142.5345679772105), ('"ABCC1"', 142.12767533080378), ('"PCDH1"', 141.88574781859327), ('"KLHL24"', 141.86203996084208), ('"SH3BP5"', 141.06078487715848), ('"MTHFD1"', 141.0395416466391), ('"RHOV"', 140.99642851291043), ('"H2AJ"', 140.73856272173148), ('"DHRS3"', 140.7140197448024), ('"CYFIP2"', 140.4011057494659), ('"SMAD3"', 139.80982057678975), ('"GPSM2"', 139.26410974586997), ('"NFIL3"', 138.6979283520023), ('"DBN1"', 138.5000260367084), ('"LYPD6B"', 137.8817124945893), ('"CD44"', 137.78622380234137), ('"CLIC3"', 136.21918010154582), ('"ZNF84"', 136.11779539206862), ('"PRR11"', 135.8069509663006), ('"MCM4"', 135.78203132768073), ('"BSCL2"', 135.75244647653324), ('"DLL1"', 135.7460147744456)]
520
We can notice that the first three genes are the same in both cases: GDPH, ACTG1 and ALDOA
normoxia_samples_summary = normoxia_samples.describe()
normo_genes = normoxia_samples_summary.loc['mean'][normoxia_samples_summary.loc['mean'] > 92.814]
normo_genes_sorted = sorted(normo_genes.items(), key=lambda x: x[1], reverse=True)
print(normo_genes_sorted)
print(len(normo_genes_sorted)) # 538 genes overcome the mean threshold
normo_genes_std = normoxia_samples_summary.loc['std'][normoxia_samples_summary.loc['std'] > 75.526]
normo_genes_std_sorted = sorted(normo_genes_std.items(), key=lambda x: x[1], reverse=True)
print(normo_genes_std_sorted)
print(len(normo_genes_std_sorted)) ## 642 genes overcome the mean std threshold
[('"MT-CO1"', 8972.230158730159), ('"GAPDH"', 8623.079365079366), ('"ALDOA"', 5280.253968253968), ('"ACTG1"', 5217.81746031746), ('"MT-CYB"', 5064.5317460317465), ('"MT-CO2"', 4927.166666666667), ('"SULF2"', 3915.095238095238), ('"MT-ND4"', 3458.222222222222), ('"FTH1"', 3254.8650793650795), ('"BEST1"', 2862.095238095238), ('"CTSD"', 2753.2936507936506), ('"MT-CO3"', 2732.746031746032), ('"MT-RNR2"', 2682.1507936507937), ('"DDX5"', 2410.15873015873), ('"XBP1"', 2162.063492063492), ('"TMSB4X"', 2132.9841269841268), ('"TUBA1B"', 2093.404761904762), ('"SLC9A3R1"', 2052.5873015873017), ('"GATA3"', 1894.6825396825398), ('"SLC39A6"', 1805.388888888889), ('"SCD"', 1784.6746031746031), ('"COX6C"', 1780.7142857142858), ('"FTL"', 1736.920634920635), ('"HSPA5"', 1698.126984126984), ('"MT-ATP6"', 1669.9444444444443), ('"MT-ND5"', 1650.2222222222222), ('"FOS"', 1635.047619047619), ('"UBC"', 1625.4761904761904), ('"HSPA8"', 1606.873015873016), ('"HSPB1"', 1583.3412698412699), ('"NME1-NME2"', 1551.920634920635), ('"IDH2"', 1475.373015873016), ('"UBB"', 1473.3809523809523), ('"TFF1"', 1454.3015873015872), ('"GFRA1"', 1425.9285714285713), ('"NCOA3"', 1394.7539682539682), ('"PGK1"', 1379.4603174603174), ('"TUBB4B"', 1207.5714285714287), ('"CLDN4"', 1206.3333333333333), ('"DHCR7"', 1181.4603174603174), ('"FLNA"', 1146.2777777777778), ('"SLC1A5"', 1128.626984126984), ('"LDHA"', 1116.484126984127), ('"ATP1A1"', 1083.3095238095239), ('"TUBB"', 1077.8015873015872), ('"PSAP"', 1077.357142857143), ('"KPNA2"', 1075.7857142857142), ('"LMNA"', 1047.9761904761904), ('"GPI"', 1013.5952380952381), ('"MCM7"', 1003.6031746031746), ('"DYNC2I2"', 1000.6666666666666), ('"GRN"', 969.6111111111111), ('"HNRNPA2B1"', 968.0238095238095), ('"H2AZ1"', 950.2777777777778), ('"GLUL"', 934.0952380952381), ('"SLC25A39"', 927.8015873015873), ('"NME1"', 926.8968253968254), ('"CCND1"', 921.968253968254), ('"S100A11"', 915.3174603174604), ('"DSCAM-AS1"', 858.4206349206349), ('"PEBP1"', 843.6746031746031), ('"EMP2"', 834.6111111111111), ('"NR4A1"', 830.2936507936508), ('"NECTIN2"', 808.1269841269841), ('"JUP"', 791.936507936508), ('"GSTM3"', 782.5396825396825), ('"TMSB10"', 771.2777777777778), ('"ANXA2"', 763.2619047619048), ('"TMEM64"', 757.8809523809524), ('"ARPC1B"', 757.2460317460317), ('"DUSP1"', 753.6746031746031), ('"CRIP2"', 742.7539682539683), ('"FKBP4"', 739.4603174603175), ('"SQSTM1"', 728.8809523809524), ('"UGDH"', 718.3730158730159), ('"STARD10"', 717.0952380952381), ('"ARL6IP1"', 712.1587301587301), ('"FBP1"', 709.452380952381), ('"GSN"', 704.6825396825396), ('"KIF22"', 695.7301587301587), ('"AARS1"', 691.8095238095239), ('"NQO1"', 691.1587301587301), ('"SLC3A2"', 679.1190476190476), ('"MYBL2"', 648.3174603174604), ('"ACTN1"', 641.3253968253969), ('"PLK2"', 639.5), ('"HSP90AA1"', 636.1428571428571), ('"DHCR24"', 627.4126984126984), ('"TACSTD2"', 607.2063492063492), ('"THBS1"', 606.7142857142857), ('"CALM2"', 606.5873015873016), ('"MAGED2"', 591.1428571428571), ('"CYBA"', 574.6984126984127), ('"ACKR3"', 571.7380952380952), ('"CRABP2"', 568.3174603174604), ('"S100A10"', 568.0555555555555), ('"TK1"', 567.531746031746), ('"AGR2"', 559.6349206349206), ('"MALAT1"', 558.3888888888889), ('"DNAJB1"', 542.8968253968254), ('"TOB1"', 539.5555555555555), ('"PDXK"', 535.8412698412699), ('"MBTPS1"', 535.3809523809524), ('"IER2"', 532.7063492063492), ('"DSP"', 514.4920634920635), ('"CD9"', 510.484126984127), ('"SARS1"', 509.25396825396825), ('"CXXC5"', 491.07936507936506), ('"GPAA1"', 489.05555555555554), ('"MTHFD1"', 475.0), ('"MT-RNR1"', 474.4047619047619), ('"LY6E"', 473.1507936507937), ('"SPTSSB"', 470.76984126984127), ('"FDPS"', 458.8492063492063), ('"STMN1"', 456.5), ('"ISOC1"', 454.26190476190476), ('"PRMT6"', 451.7142857142857), ('"MTATP6P1"', 449.7936507936508), ('"MCM2"', 447.5079365079365), ('"MCM3"', 447.08730158730157), ('"MLPH"', 446.9920634920635), ('"BMP7"', 443.42063492063494), ('"TPD52L1"', 442.6111111111111), ('"TPM4"', 441.63492063492066), ('"H2AJ"', 441.484126984127), ('"FLNB"', 435.6904761904762), ('"CLU"', 432.07936507936506), ('"PTPRF"', 429.46031746031747), ('"NINJ1"', 429.35714285714283), ('"IARS1"', 425.04761904761904), ('"LDOC1"', 419.484126984127), ('"NUP188"', 415.8253968253968), ('"GREB1"', 412.9920634920635), ('"BSCL2"', 412.531746031746), ('"UCP2"', 411.031746031746), ('"SYTL2"', 408.3015873015873), ('"ELP2"', 402.13492063492066), ('"CA12"', 400.35714285714283), ('"EEF1A2"', 395.8015873015873), ('"IFITM3"', 394.05555555555554), ('"APEH"', 393.14285714285717), ('"SREBF1"', 388.8095238095238), ('"MAGED1"', 386.63492063492066), ('"INSIG1"', 385.968253968254), ('"PFKP"', 384.8809523809524), ('"MCM4"', 374.72222222222223), ('"LDLR"', 373.23809523809524), ('"TUBA1C"', 370.8968253968254), ('"PREX1"', 368.8015873015873), ('"SELENBP1"', 365.42063492063494), ('"AHNAK"', 365.13492063492066), ('"CDKN1A"', 364.8968253968254), ('"TFRC"', 364.6507936507937), ('"PPM1D"', 363.468253968254), ('"DNAJA1"', 363.3015873015873), ('"HMGB2"', 361.3095238095238), ('"PRSS23"', 357.36507936507934), ('"CCNB1"', 355.6666666666667), ('"IPO4"', 355.1190476190476), ('"PRC1"', 354.6031746031746), ('"ZWINT"', 354.3888888888889), ('"CBX5"', 354.05555555555554), ('"FDFT1"', 353.8888888888889), ('"DDIT4"', 344.96031746031747), ('"SRM"', 344.72222222222223), ('"CHPF"', 344.1031746031746), ('"NFKBIA"', 343.94444444444446), ('"KDM5B"', 340.92063492063494), ('"ZDHHC12"', 339.03968253968253), ('"CDC20"', 336.5), ('"DCXR"', 333.76190476190476), ('"PSME2"', 333.1825396825397), ('"IGFBP2"', 332.54761904761904), ('"DEGS2"', 330.26984126984127), ('"TNFRSF12A"', 325.6190476190476), ('"LAMB2"', 324.26984126984127), ('"IGFBP4"', 324.14285714285717), ('"MRNIP"', 323.9047619047619), ('"EBP"', 322.5), ('"GPRC5A"', 322.42063492063494), ('"DKK1"', 322.23015873015873), ('"TXNDC5"', 321.484126984127), ('"SNRNP25"', 320.58730158730157), ('"MYH9"', 320.54761904761904), ('"TM7SF2"', 320.43650793650795), ('"BLOC1S5-TXNDC5"', 319.75396825396825), ('"PIEZO1"', 318.1111111111111), ('"ZFP36L1"', 317.4920634920635), ('"AP1B1"', 317.1190476190476), ('"JUNB"', 316.77777777777777), ('"SLC52A2"', 315.6825396825397), ('"RBM14"', 311.7936507936508), ('"ITGB5"', 310.13492063492066), ('"DDR1"', 306.56349206349205), ('"FEN1"', 306.1507936507937), ('"SEMA3C"', 305.43650793650795), ('"TMEM106C"', 304.9047619047619), ('"ELAPOR1"', 300.07142857142856), ('"H4C3"', 298.6190476190476), ('"ACLY"', 298.23809523809524), ('"TIMELESS"', 298.1111111111111), ('"LSS"', 297.015873015873), ('"CYP1B1"', 296.07936507936506), ('"SMIM14"', 293.76984126984127), ('"TUBA1A"', 293.6825396825397), ('"EFNA1"', 292.75396825396825), ('"RAB31"', 291.74603174603175), ('"SQLE"', 285.3888888888889), ('"ITGB4"', 282.7857142857143), ('"PLK1"', 279.6984126984127), ('"TUBG1"', 279.484126984127), ('"WARS1"', 279.1746031746032), ('"PTP4A1"', 276.92857142857144), ('"HES1"', 276.8492063492063), ('"CDH3"', 276.031746031746), ('"SLC38A2"', 274.5079365079365), ('"BAMBI"', 272.1904761904762), ('"MYC"', 270.56349206349205), ('"MVD"', 269.531746031746), ('"ISG15"', 268.1587301587302), ('"NR2F2"', 268.0), ('"LINC00052"', 267.6825396825397), ('"TSPAN15"', 266.63492063492066), ('"PGP"', 266.14285714285717), ('"TRIB3"', 265.13492063492066), ('"PPP1R15A"', 261.515873015873), ('"RHOBTB3"', 259.1825396825397), ('"KRT80"', 258.42063492063494), ('"LYPD3"', 258.2857142857143), ('"ID3"', 257.3968253968254), ('"ASF1B"', 256.86507936507934), ('"AMOTL2"', 256.26984126984127), ('"PRSS8"', 256.0), ('"TRAFD1"', 254.35714285714286), ('"AMIGO2"', 253.18253968253967), ('"ASS1"', 249.38095238095238), ('"DDX41"', 247.22222222222223), ('"FXYD3"', 245.22222222222223), ('"OLFM1"', 243.83333333333334), ('"MT2A"', 243.77777777777777), ('"BHLHE40"', 243.13492063492063), ('"SLC7A5"', 242.3968253968254), ('"H2BC21"', 242.3968253968254), ('"DTYMK"', 242.32539682539684), ('"S100A13"', 239.70634920634922), ('"LPCAT1"', 239.15873015873015), ('"SLC2A1"', 237.38888888888889), ('"ILVBL"', 235.4047619047619), ('"ADGRG1"', 235.1984126984127), ('"NUP210"', 234.3968253968254), ('"PIM3"', 234.3095238095238), ('"FANCI"', 233.36507936507937), ('"ANXA9"', 232.79365079365078), ('"CLK3"', 232.54761904761904), ('"MCM5"', 231.86507936507937), ('"MT-ATP8"', 231.1031746031746), ('"UBE2C"', 228.82539682539684), ('"RRM2"', 228.72222222222223), ('"CKAP5"', 225.3015873015873), ('"OSR2"', 224.3015873015873), ('"XPOT"', 224.20634920634922), ('"GPER1"', 223.0079365079365), ('"LIG1"', 222.77777777777777), ('"SLC25A10"', 222.65079365079364), ('"ZMYND8"', 217.61111111111111), ('"PYGB"', 217.1031746031746), ('"ITPK1"', 216.76984126984127), ('"UNG"', 215.57142857142858), ('"RNASEH2A"', 214.3015873015873), ('"SPAG5"', 214.1031746031746), ('"CA2"', 212.5952380952381), ('"THSD8"', 212.515873015873), ('"KLF10"', 211.0793650793651), ('"RNF26"', 210.70634920634922), ('"TPM1"', 209.81746031746033), ('"TPX2"', 208.5952380952381), ('"AMFR"', 208.55555555555554), ('"IFITM2"', 207.24603174603175), ('"ERBB3"', 207.0079365079365), ('"SLC9A3R1-AS1"', 205.83333333333334), ('"TBC1D9"', 205.22222222222223), ('"PC"', 203.84920634920636), ('"ATP9A"', 201.65873015873015), ('"RHOD"', 200.55555555555554), ('"GINS2"', 200.17460317460316), ('"ACAT2"', 200.1031746031746), ('"FNDC10"', 199.88095238095238), ('"BAG3"', 198.11111111111111), ('"PKMYT1"', 197.6984126984127), ('"NCAPD2"', 196.20634920634922), ('"AURKA"', 195.34920634920636), ('"FLOT2"', 195.34126984126985), ('"ESR1"', 193.76190476190476), ('"CD59"', 191.45238095238096), ('"TFF3"', 190.45238095238096), ('"LGALS3BP"', 189.88095238095238), ('"PHLDA1"', 189.72222222222223), ('"RFC2"', 189.66666666666666), ('"ACOT7"', 188.53968253968253), ('"MCM6"', 187.6031746031746), ('"SPDEF"', 187.26984126984127), ('"ADAM15"', 187.20634920634922), ('"FADS1"', 186.15873015873015), ('"ELF3"', 185.0793650793651), ('"MOV10"', 185.0079365079365), ('"VARS1"', 184.79365079365078), ('"ATAD2"', 183.015873015873), ('"RECQL4"', 182.92857142857142), ('"CACNG4"', 182.76190476190476), ('"NR4A1AS"', 182.43650793650792), ('"CERCAM"', 181.75396825396825), ('"KIF20A"', 181.65873015873015), ('"TSC22D3"', 181.54761904761904), ('"TMEM109"', 181.04761904761904), ('"IFI30"', 180.24603174603175), ('"NOP2"', 180.17460317460316), ('"PCK2"', 179.9047619047619), ('"STC1"', 178.61904761904762), ('"BAG1"', 177.11904761904762), ('"PDLIM7"', 176.4126984126984), ('"IFT122"', 176.0873015873016), ('"HSPB8"', 175.02380952380952), ('"KYNU"', 173.73809523809524), ('"FAM102A"', 173.62698412698413), ('"RGS16"', 173.46031746031747), ('"GADD45B"', 173.13492063492063), ('"KLF6"', 172.65079365079364), ('"NCAPG2"', 171.9920634920635), ('"INTS1"', 171.47619047619048), ('"IER5"', 171.36507936507937), ('"SFXN2"', 171.20634920634922), ('"STC2"', 170.36507936507937), ('"BOP1"', 169.29365079365078), ('"PHLDA3"', 167.06349206349208), ('"CCND3"', 166.984126984127), ('"IGF1R"', 165.36507936507937), ('"ADCY1"', 165.03174603174602), ('"KCNK6"', 164.95238095238096), ('"H2AX"', 164.84920634920636), ('"TUBD1"', 164.5793650793651), ('"SLC25A29"', 163.8015873015873), ('"DOLK"', 162.9920634920635), ('"PTTG1"', 162.9126984126984), ('"G6PC3"', 161.73015873015873), ('"PCDH10"', 160.9920634920635), ('"TUFT1"', 159.11111111111111), ('"SSH3"', 159.015873015873), ('"PFKFB3"', 157.76984126984127), ('"FASN"', 157.53174603174602), ('"CCNB2"', 156.5793650793651), ('"RAMP3"', 155.93650793650792), ('"COL18A1"', 155.015873015873), ('"BCAR1"', 154.93650793650792), ('"GYS1"', 154.70634920634922), ('"SCARB1"', 154.1031746031746), ('"TLE1"', 153.46031746031747), ('"NR4A2"', 152.84920634920636), ('"DHTKD1"', 152.8095238095238), ('"DSCAM"', 150.984126984127), ('"POLD1"', 150.64285714285714), ('"MATN2"', 150.11904761904762), ('"DNMT1"', 149.68253968253967), ('"H2AC6"', 149.61111111111111), ('"S100A16"', 149.1984126984127), ('"MYADM"', 148.84126984126985), ('"PCNA"', 148.4206349206349), ('"UBE2S"', 147.26190476190476), ('"PKP3"', 147.25396825396825), ('"MDK"', 145.76984126984127), ('"ABCC5"', 145.76190476190476), ('"RRM1"', 145.23809523809524), ('"CYB561A3"', 145.16666666666666), ('"SULT2B1"', 143.62698412698413), ('"RASD1"', 143.61904761904762), ('"CRELD2"', 141.61111111111111), ('"NT5DC2"', 141.55555555555554), ('"WFS1"', 140.9920634920635), ('"ATP2A3"', 140.4126984126984), ('"NEAT1"', 139.78571428571428), ('"TRIB1"', 139.65079365079364), ('"CELSR2"', 139.44444444444446), ('"ARMC6"', 139.35714285714286), ('"SOX4"', 139.03968253968253), ('"SLC29A1"', 138.23015873015873), ('"BIRC5"', 138.22222222222223), ('"BRIP1"', 137.92857142857142), ('"PINK1"', 137.24603174603175), ('"TSKU"', 136.9920634920635), ('"EGR1"', 136.8968253968254), ('"ISYNA1"', 136.6984126984127), ('"RAMP1"', 136.65873015873015), ('"HERPUD1"', 136.65873015873015), ('"CAPG"', 134.95238095238096), ('"SDF2L1"', 134.75396825396825), ('"IFITM1"', 134.1031746031746), ('"H1-0"', 133.95238095238096), ('"FANCA"', 133.64285714285714), ('"TELO2"', 133.13492063492063), ('"SPTBN1"', 132.54761904761904), ('"SAPCD2"', 132.1031746031746), ('"UHRF1"', 131.97619047619048), ('"MXD4"', 131.34920634920636), ('"QPRT"', 131.23809523809524), ('"AURKB"', 131.17460317460316), ('"XPC"', 129.76190476190476), ('"TGIF1"', 129.75396825396825), ('"ZFP36L2"', 129.4047619047619), ('"NDST1"', 129.07142857142858), ('"MKI67"', 128.23809523809524), ('"RRP12"', 127.96031746031746), ('"PYCR3"', 127.36507936507937), ('"FADS3"', 127.02380952380952), ('"UBE2T"', 126.46825396825396), ('"CKS2"', 125.2063492063492), ('"PPRC1"', 124.5079365079365), ('"RUSC1"', 124.41269841269842), ('"EFEMP1"', 124.2063492063492), ('"SLC37A4"', 124.11904761904762), ('"ST6GALNAC2"', 123.74603174603175), ('"RANGAP1"', 123.73809523809524), ('"HYAL2"', 123.39682539682539), ('"SLC6A14"', 123.28571428571429), ('"PXDN"', 123.11111111111111), ('"TOP2A"', 122.53174603174604), ('"FAM234B"', 122.38095238095238), ('"TUBB3"', 122.17460317460318), ('"AVPI1"', 122.07142857142857), ('"OBSL1"', 121.80952380952381), ('"PLPP2"', 121.69047619047619), ('"CYP1B1-AS1"', 121.12698412698413), ('"SH3BP5"', 120.93650793650794), ('"EFHD1"', 120.7063492063492), ('"ELOVL5"', 120.22222222222223), ('"DBN1"', 120.21428571428571), ('"HLA-C"', 120.0952380952381), ('"ECE1"', 119.61904761904762), ('"ZNF331"', 119.53174603174604), ('"CTPS1"', 119.5), ('"QSOX1"', 118.86507936507937), ('"GPX3"', 118.72222222222223), ('"KIF2C"', 118.37301587301587), ('"ANLN"', 118.05555555555556), ('"BNIP3"', 117.83333333333333), ('"CSRNP1"', 117.76190476190476), ('"ABHD14A-ACY1"', 117.34126984126983), ('"ID2"', 117.15079365079364), ('"AEN"', 116.36507936507937), ('"TROAP"', 115.4920634920635), ('"RHOV"', 115.41269841269842), ('"PHGDH"', 115.1984126984127), ('"CDCA5"', 114.82539682539682), ('"CGN"', 114.5), ('"PIK3R3"', 114.43650793650794), ('"POMGNT1"', 114.35714285714286), ('"ITPRID2"', 114.23809523809524), ('"HSPA1A"', 114.02380952380952), ('"SLC27A4"', 113.92063492063492), ('"NOL3"', 113.51587301587301), ('"TEDC1"', 112.80952380952381), ('"AREG"', 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143.24470970036853), ('"H2AC6"', 142.9452467050078), ('"CAPG"', 142.31890146528576), ('"CACNG4"', 142.1628603297741), ('"ISYNA1"', 141.69190632305123), ('"RHOV"', 141.5715943170109), ('"NUSAP1"', 141.4123465120063), ('"TUBB3"', 140.7364674483528), ('"RFC2"', 140.55683547946003), ('"THSD4"', 139.87757481636558), ('"BAG1"', 139.158936882565), ('"PKP3"', 137.69659031409228), ('"FAM102A"', 137.4475163327142), ('"TPM1"', 136.85270334450252), ('"ANKZF1"', 136.05273604057726), ('"DTYMK"', 135.60370669654006), ('"SOX4"', 135.19499403712553), ('"PC"', 134.57821918633448), ('"PYGB"', 134.06582439175645), ('"RNASEH2A"', 133.69267862325265), ('"FADS1"', 133.57289621466853), ('"ANLN"', 133.2961998291357), ('"UBE2S"', 133.0940827277564), ('"TACC3"', 133.00401068066645), ('"ABCC5"', 132.8687429651642), ('"NEDD9"', 132.4315370295157), ('"THSD8"', 132.37968026110258), ('"IER5"', 132.11441121540696), ('"POLD1"', 132.02669210645035), ('"CSRNP1"', 131.8438730360378), ('"PXDN"', 131.2579275912718), ('"CREB3L4"', 131.03081644080092), ('"PHLDA1-AS1"', 130.99088639084903), ('"SLC9A3R1-AS1"', 130.7521472099024), ('"AVPI1"', 130.5156651790997), ('"CDCA5"', 128.98099577007955), ('"HMCN1"', 128.1376581203645), ('"BIRC5"', 127.97136485254129), ('"PDLIM7"', 126.76221959819225), ('"PINK1"', 126.57511202494346), ('"ELOVL5"', 126.01059567442029), ('"FAM83D"', 125.05188764338834), ('"H2AX"', 124.92763136858507), ('"MUC5B"', 124.9087306161064), ('"CYP1B1-AS1"', 124.62532546008354), ('"DLG5"', 124.24204155151708), ('"SLC25A29"', 124.23867480563499), ('"RET"', 124.21734152890228), ('"COL18A1"', 124.03663872433721), ('"RHOD"', 123.77576858532886), ('"MXD4"', 123.25630644865632), ('"PHLDA3"', 122.48537846007552), ('"GYS1"', 122.21443891523244), ('"MAP3K1"', 122.19737897586607), ('"ATP2A3"', 121.9705059326242), ('"PSRC1"', 121.96819387635584), ('"INTS1"', 121.89206466612754), ('"FAM234B"', 121.49415506223218), ('"HERPUD1"', 121.15181634286219), ('"XPOT"', 120.51099982725678), ('"CERCAM"', 120.4636168481047), ('"RACGAP1"', 120.23820933516508), ('"PGP"', 120.17938021379304), ('"NOP2"', 119.79376139783434), ('"SMTN"', 119.71782829929053), ('"SLC25A10"', 119.48196968314961), ('"DDX41"', 119.31470245624479), ('"MT-ATP8"', 119.1576991630892), ('"TGIF1"', 117.66485874774585), ('"SPTBN1"', 117.05285009040024), ('"COL4A5"', 116.83951522263493), ('"ID1"', 116.04943336409268), ('"SEMA3B"', 115.79475657728916), ('"SH3BP5"', 115.54841382082203), ('"MYADM"', 115.20426469178388), ('"DBN1"', 115.17128858481057), ('"ACOT7"', 115.1019479100958), ('"WARS1"', 114.52294647729457), ('"PCK2"', 113.36845618223289), ('"ADAM15"', 113.30548565433664), ('"TMTC1"', 113.0618934302435), ('"E2F1"', 112.65270496755916), ('"CKS2"', 112.6051023682545), ('"VARS1"', 112.49180005389314), ('"BUB1"', 112.01124319984412), ('"BCAR1"', 112.00626739833773), ('"FANCA"', 111.78210692490738), ('"SYT12"', 111.22723458886186), ('"ADCY1"', 110.7482143608961), ('"ATP9A"', 110.45383924144333), ('"TRIM16"', 110.28203641371316), ('"SLC35C1"', 110.10144384388397), ('"SCARB1"', 109.90256261726235), ('"MAOB"', 109.19549179600507), ('"PAQR4"', 108.8422700334376), ('"CLK3"', 108.76202330908394), ('"DUSP2"', 108.38621830039244), ('"RRM1"', 108.1960759784885), ('"CDCA8"', 108.06930639236558), ('"ALCAM"', 107.90187076360496), ('"KNSTRN"', 107.7298101734148), ('"TMPRSS4"', 107.72270456763098), ('"TUBD1"', 106.98854915734506), ('"NAPRT"', 106.54642150963086), ('"CDCA3"', 106.16513522106932), ('"N4BP3"', 105.68089494111771), ('"CCN1"', 105.4939101529803), ('"MOV10"', 105.45220688306118), ('"ACAT2"', 105.36814162659067), ('"MIR4435-2HG"', 105.35687259583307), ('"WFS1"', 105.30059798741853), ('"TMEM109"', 104.82372686699186), ('"KIF23"', 104.71303431548317), ('"NT5DC2"', 104.65322206644612), ('"CALCOCO1"', 104.63251399123973), ('"PHGDH"', 104.5227645896353), ('"MUC16"', 104.28119646661101), ('"DSCAM"', 104.12991763192629), ('"CDC45"', 104.05355763808781), ('"IRF9"', 104.03612071150296), ('"RFX5"', 103.73499723234029), ('"KCNE4"', 103.57049926238801), ('"IGF1R"', 103.53941109931836), ('"CEACAM6"', 103.42047477736006), ('"CCND3"', 102.41187307159139), ('"DNMT1"', 102.4042695042468), ('"PPRC1"', 102.30618718585859), ('"SEMA4C"', 102.0666812851845), ('"RRP12"', 101.8147651998393), ('"FLOT2"', 101.6880061913626), ('"SLC39A8"', 101.65246114651066), ('"MALRD1"', 101.62759436544752), ('"SEMA4B"', 101.60428843592888), ('"H1-0"', 101.54225580656414), ('"UBE2T"', 101.23285526017222), ('"MVP"', 100.23947484629153), ('"SLC29A1"', 100.02121076638994), ('"TSC22D2"', 99.50945109457119), ('"PLPP2"', 99.10805935226172), ('"PIMREG"', 98.90342636143471), ('"TMEM164"', 98.60394145185714), ('"ALDH3B2"', 98.55547305878692), ('"BOP1"', 98.5317871519901), ('"FNDC10"', 97.88929315449018), ('"MCM10"', 97.61510681941132), ('"CGN"', 97.6090774467211), ('"NRP1"', 97.36608092290513), ('"MUC5AC"', 97.32491679200878), ('"TLE1"', 97.18038080136553), ('"POLA2"', 97.16498193426331), ('"HSPA6"', 97.15603944171458), ('"RANGAP1"', 97.14757257462928), ('"ZYX"', 96.97433108245131), ('"HSPA1A"', 96.97252924705755), ('"TRIP6"', 96.4380745464271), ('"MX1"', 96.30412804945108), ('"GCHFR"', 96.28392702406047), ('"NDST1"', 96.02642790993973), ('"DHTKD1"', 95.77680005393493), ('"S100P"', 95.7101606787014), ('"PAPSS2"', 94.96527184788577), ('"GPX3"', 94.69543928945163), ('"KANK2"', 94.69039541646757), ('"POLE"', 94.54073716724967), ('"QPRT"', 94.53324736378657), ('"DLC1"', 94.09098636046768), ('"WDR62"', 93.87106050322431), ('"AEN"', 93.70418160783248), ('"CDCA4"', 93.49527583345913), ('"ALDOC"', 93.49291799000454), ('"QSOX1"', 92.98725531379905), ('"NBEAL2"', 92.90471684849167), ('"CDT1"', 92.8602653931642), ('"HYAL2"', 92.42770834463695), ('"PIK3R3"', 92.40627649953187), ('"L1CAM"', 92.24660427354496), ('"CLDN9"', 92.08671999805401), ('"SSH3"', 91.99382449942901), ('"DOLK"', 91.98499843185265), ('"ARMC9"', 91.91259063621433), ('"SLC2A10"', 91.7284467136991), ('"IQGAP3"', 91.50923624126868), ('"FOXM1"', 91.26482936687651), ('"OSER1"', 90.94809264385677), ('"OBSL1"', 90.80482051395417), ('"FAM102B"', 90.67602367683128), ('"SLC27A4"', 90.67388626718086), ('"APBB2"', 90.55702614445212), ('"SYTL5"', 90.29657976612579), ('"HSH2D"', 89.68814541828498), ('"ARRDC3"', 89.21065919656529), ('"ESPL1"', 88.97217720984648), ('"SDC1"', 88.64081255716043), ('"PKIB"', 88.55114278702949), ('"S100A14"', 88.18554789274066), ('"CAD"', 88.02014379836986), ('"RMI2"', 87.92372307497607), ('"JUN"', 87.8877917375783), ('"CRELD2"', 87.87682035415003), ('"FADS3"', 87.84281091000804), ('"C6orf141"', 87.59598780403746), ('"HIGD1A"', 87.58128269993011), ('"NRARP"', 86.9916620708058), ('"GALNT6"', 86.77595782901422), ('"GADD45G"', 86.45495937191805), ('"G6PC3"', 86.40994504786242), ('"CTPS1"', 86.39377292374724), ('"PFKFB4"', 86.32361555898024), ('"XPC"', 86.3208367495523), ('"HJURP"', 86.20675331701293), ('"BTG2"', 86.15556988507599), ('"CDC25B"', 85.75513645590817), ('"REEP4"', 85.74359118074906), ('"TYK2"', 85.38706827483556), ('"ELOVL2"', 85.19950555039476), ('"GEM"', 85.13282134312982), ('"CYB561A3"', 85.02204420031319), ('"BCL6"', 84.99742853253186), ('"ANXA6"', 84.93731470073342), ('"MMP17"', 84.66930029649714), ('"CCNA2"', 84.6470697230472), ('"MADD"', 84.5021799286765), ('"CKB"', 83.93913781510302), ('"ITPKC"', 83.91818994219669), ('"TELO2"', 83.82089030065029), ('"SULT2B1"', 83.75504609294742), ('"GPSM2"', 83.73314414597978), ('"ATP13A2"', 83.70765453990113), ('"DNAJC22"', 83.53467372466189), ('"ABCA3"', 83.3848846262138), ('"BTG1"', 83.30563654018304), ('"EPS8L1"', 83.0896276452255), ('"PTGES"', 83.08418985086519), ('"PRR11"', 82.22745882080382), ('"TEDC1"', 81.88603927759254), ('"TONSL"', 81.46100868825486), ('"ALDH4A1"', 80.91740841207772), ('"FANCG"', 80.57370676729613), ('"SLC37A4"', 80.45317715470108), ('"EFHD1"', 79.97410255429617), ('"CELSR1"', 79.94633160131824), ('"SFXN3"', 79.91851921980111), ('"NOL3"', 79.8832882775349), ('"ABCA12"', 79.85624226428033), ('"TRMT2A"', 79.81601541959056), ('"NELL2"', 79.65917239107968), ('"PPIF"', 79.6378872105801), ('"SGSM2"', 79.3258579911478), ('"DSG2"', 79.26583122632348), ('"CAV2"', 79.25855779742015), ('"ECE1"', 79.15936908721363), ('"NOTCH2"', 79.10658271327), ('"BNIP3L"', 78.73980982192965), ('"DIP2C"', 78.65133870040025), ('"CHTF18"', 78.6353319352785), ('"TMEM94"', 78.41916999192368), ('"CXCL12"', 78.32929472871827), ('"PRC1-AS1"', 78.32702953358792), ('"TMEM150C"', 78.10070056389654), ('"ARHGEF10L"', 77.99871427511758), ('"DBP"', 77.8408029453358), ('"ETS2"', 77.83357418133431), ('"OVOL1"', 77.44410662402694), ('"PIK3IP1"', 77.40436680188009), ('"TRIP13"', 77.03572290291436), ('"NME3"', 76.92614474956964), ('"P4HA2"', 76.7689243511445), ('"FOSL2"', 76.72329709716804), ('"FHL2"', 76.72196763287758), ('"MT1X"', 76.63771917274155), ('"FASN"', 76.57526352633062), ('"GATA3-AS1"', 76.55880652898695), ('"ARMC6"', 76.45018919905579), ('"TXNRD1"', 76.31241052410807), ('"KIAA0513"', 76.2657280162018), ('"SIGMAR1"', 76.18367987545022), ('"DUSP4"', 76.0943007272858), ('"LRP8"', 75.99187926539074)]
642
Here we can notice that the first 4 genes are the same, although in different orders: MT-CO1, GAPDH, ALDOA, ACTG1
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_summary.transpose()
normoxia_summary_t = normoxia_summary.transpose()
# Calculate the difference in means
diff_mean = abs(hypoxia_summary_t['mean'] - normoxia_summary_t['mean'])
# Sort genes by difference in mean in descending order
sorted_genes = diff_mean.sort_values(ascending=False)
# Get the list of genes
genes_list = sorted_genes.index.tolist()
genes_list
# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes.reset_index()
# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']
# Display the DataFrame
sorted_genes_df[:50]
| Gene | Difference | |
|---|---|---|
| 0 | "GAPDH" | 37668.952893 |
| 1 | "ACTG1" | 21697.537378 |
| 2 | "ALDOA" | 20955.116999 |
| 3 | "CYP1B1" | 10400.114183 |
| 4 | "FTH1" | 7711.505888 |
| 5 | "PGK1" | 7316.789683 |
| 6 | "BEST1" | 6846.920891 |
| 7 | "MT-CO1" | 6474.568868 |
| 8 | "GPI" | 6124.719278 |
| 9 | "UBC" | 5320.701229 |
| 10 | "LDHA" | 4750.765873 |
| 11 | "MT-CYB" | 4408.886585 |
| 12 | "DDIT4" | 4318.628392 |
| 13 | "CYP1B1-AS1" | 4309.364951 |
| 14 | "MT-CO2" | 3872.005376 |
| 15 | "CYP1A1" | 3232.800179 |
| 16 | "FTL" | 3192.740655 |
| 17 | "PFKFB3" | 2697.883385 |
| 18 | "MT-CO3" | 2375.729903 |
| 19 | "SLC2A1" | 2293.482079 |
| 20 | "TMSB10" | 2205.593190 |
| 21 | "BNIP3" | 2091.755376 |
| 22 | "KRT80" | 1959.474526 |
| 23 | "SULF2" | 1934.329109 |
| 24 | "DSP" | 1926.701485 |
| 25 | "SLC3A2" | 1855.913210 |
| 26 | "SLC9A3R1" | 1785.143753 |
| 27 | "TUBA1B" | 1762.235407 |
| 28 | "HK2" | 1692.755120 |
| 29 | "BHLHE40" | 1492.332821 |
| 30 | "XBP1" | 1448.111879 |
| 31 | "DDX5" | 1435.344214 |
| 32 | "MT-ATP6" | 1380.299283 |
| 33 | "SLC39A6" | 1275.856631 |
| 34 | "SQSTM1" | 1261.127112 |
| 35 | "NDRG1" | 1185.033922 |
| 36 | "GFRA1" | 1179.428571 |
| 37 | "PFKP" | 1164.312596 |
| 38 | "VEGFA" | 1129.327061 |
| 39 | "GATA3" | 1103.247056 |
| 40 | "CDKN1A" | 1084.256400 |
| 41 | "STC2" | 1082.973630 |
| 42 | "COX6C" | 1039.101382 |
| 43 | "NME1-NME2" | 970.646441 |
| 44 | "TRIB3" | 940.171531 |
| 45 | "NCOA3" | 936.334741 |
| 46 | "ANXA2" | 927.302611 |
| 47 | "FLNA" | 925.843190 |
| 48 | "DHCR7" | 908.637737 |
| 49 | "PSAP" | 908.481567 |
GAPDH - As discussed earlier, GAPDH is heavily involved in glycolysis and often upregulated in hypoxia to compensate for reduced mitochondrial respiration. The large difference in expression you noted fits well with the expected biological response. ACTG1 (Actin Gamma 1) - This gene encodes a cytoskeletal protein that is crucial for cell structure and integrity. Changes in ACTG1 expression might reflect cellular restructuring in response to hypoxic stress. ALDOA (Aldolase A) - Another enzyme involved in glycolysis, facilitating the breakdown of fructose-1,6-bisphosphate into triose phosphates. Its upregulation in hypoxia is consistent with increased glycolytic flux. CYP1B1 (Cytochrome P450, Family 1, Subfamily B, Polypeptide 1) - This gene is involved in drug metabolism and the synthesis of cholesterol, steroids, and other lipids. It has been implicated in various cancers and may be involved in cellular responses to environmental stressors. FTH1 (Ferritin Heavy Chain 1) - Important for iron storage; its regulation might be linked to alterations in iron metabolism under hypoxic conditions. PGK1 (Phosphoglycerate Kinase 1) - Another glycolytic enzyme, showing expected upregulation in hypoxia to enhance anaerobic energy production. MT-CO1 - Part of the mitochondrial electron transport chain, its decreased expression in hypoxia aligns with a shift away from oxidative phosphorylation. UBC (Ubiquitin C) - Plays a role in protein degradation through the ubiquitin-proteasome system; changes in its expression may reflect altered protein turnover under stress conditions. LDHA (Lactate Dehydrogenase A) - Converts pyruvate to lactate in glycolysis, crucial under anaerobic conditions, thus its upregulation in hypoxia is expected. BNIP3 - Involved in apoptosis and mitochondrial autophagy, indicative of cellular adaptations to hypoxia-driven stress.
## Now with the std
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_summary.transpose()
normoxia_summary_t = normoxia_summary.transpose()
# Calculate the difference in means
diff_std = abs(hypoxia_summary_t['std'] - normoxia_summary_t['std'])
# Sort genes by difference in mean in descending order
sorted_genes_std = diff_std.sort_values(ascending=False)
# Get the list of genes
genes_list = sorted_genes_std.index.tolist()
genes_list
# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes_std.reset_index()
# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']
# Display the DataFrame
sorted_genes_df[:50]
| Gene | Difference | |
|---|---|---|
| 0 | "ACTG1" | 14800.715599 |
| 1 | "GAPDH" | 14023.016930 |
| 2 | "ALDOA" | 10400.225956 |
| 3 | "CYP1B1" | 8853.811534 |
| 4 | "CYP1A1" | 7704.798776 |
| 5 | "FTH1" | 7143.964639 |
| 6 | "BEST1" | 6414.440060 |
| 7 | "UBC" | 4862.663095 |
| 8 | "CYP1B1-AS1" | 3708.761495 |
| 9 | "GPI" | 3458.815260 |
| 10 | "DDIT4" | 3099.682389 |
| 11 | "IGFBP3" | 2965.667060 |
| 12 | "PGK1" | 2400.180962 |
| 13 | "HK2" | 2264.923407 |
| 14 | "SLC2A1" | 2218.865640 |
| 15 | "FTL" | 2004.389133 |
| 16 | "PFKFB3" | 1996.392563 |
| 17 | "TMSB10" | 1870.994181 |
| 18 | "LDHA" | 1845.092754 |
| 19 | "DNAJB1" | 1644.085117 |
| 20 | "SQSTM1" | 1621.860081 |
| 21 | "TFF1" | 1578.223701 |
| 22 | "BHLHE40" | 1519.021812 |
| 23 | "HSPB1" | 1398.039260 |
| 24 | "SLC7A5" | 1374.109916 |
| 25 | "XBP1" | 1372.473622 |
| 26 | "SLC3A2" | 1363.650266 |
| 27 | "MALAT1" | 1308.904278 |
| 28 | "HSPA5" | 1307.356407 |
| 29 | "MT-CYB" | 1282.562347 |
| 30 | "KRT17" | 1266.323914 |
| 31 | "NCOA3" | 1194.876389 |
| 32 | "MT-CO1" | 1177.776638 |
| 33 | "MT2A" | 1155.720993 |
| 34 | "KRT81" | 1154.928458 |
| 35 | "MT-ND4" | 1117.249721 |
| 36 | "KRT80" | 1108.771806 |
| 37 | "BNIP3" | 1064.262254 |
| 38 | "TRIB3" | 1051.400043 |
| 39 | "GDF15" | 1044.797466 |
| 40 | "COX6C" | 1009.500078 |
| 41 | "TUBA1B" | 979.218842 |
| 42 | "SLC9A3R1" | 977.321908 |
| 43 | "AARS1" | 968.278080 |
| 44 | "DSP" | 927.107657 |
| 45 | "NR4A1" | 895.658940 |
| 46 | "MT1X" | 873.276680 |
| 47 | "VEGFA" | 862.437795 |
| 48 | "ARL6IP1" | 860.600026 |
| 49 | "COTL1" | 821.729986 |
Even studying the std,the results are confirmed: the genes with higher std difference between one state and the other are stil ACTG1,GAPDH and ALDOA
#I want to do a clustering considering only genes: GAPDH, ALDOA and PGK1 as principal components
# Create a DataFrame with only the selected genes
selected_genes = ['"GAPDH"', '"ALDOA"', '"PGK1"']
selected_genes_df = df_MCF7_norm_filt.loc[selected_genes]
# Transpose the DataFrame
selected_genes_df = selected_genes_df.T
# Apply PCA to the selected genes
pca = PCA(n_components=3)
selected_genes_transformed = pca.fit_transform(selected_genes_df)
# Apply K-means clustering to the transformed data
kmeans = KMeans(n_clusters=2, random_state=42)
clusters = kmeans.fit_predict(selected_genes_transformed)
# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypo': 'red', 'Norm': 'blue'}
colors = conditions.map(color_map)
# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=colors)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('PCA of Selected Genes (GAPDH, ALDOA, PGK1) Colored by Condition')
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1. warnings.warn(
# now we want to do a k-means clustering with the selected genes
# Apply K-means clustering to the transformed data
kmeans = KMeans(n_clusters=2, random_state=42)
clusters = kmeans.fit_predict(selected_genes_transformed)
# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
###color_map = {'Hypo': 'red', 'Norm': 'blue'}
###colors = conditions.map(color_map)
cluster_colors10 = ['green' if label == 0 else 'yellow' for label in clusters]
# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=cluster_colors10)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('K-means Clustering of Selected Genes (GAPDH, ALDOA, PGK1)')
plt.show()
#i want to see the match rate of the clustering
# Assign numeric labels based on conditions
###condition_numeric = conditions.map({'C1': 0, 'C2': 1})
# Compute the confusion matrix
###cm = confusion_matrix(condition_numeric, clusters)
# Calculate the match rate using the trace of the confusion matrix
###match_rate = np.trace(cm) / np.sum(cm)
###print(f'Match Rate: {match_rate:.2%}')
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1. warnings.warn(
Again, the k-means clustering splits the data almost in the same way as the actual division. Indeed, the selection of genes chosen fof the PCA shows that GAPDH, ALDOA and PGK1 are some of the most indicated genes in order to distinguish between normoxic and hypoxic MCF7 cells.
¶
2.2.9 More unsupervised learning
X_MCF_SMART = df_trans_MCF_SMART.drop('TARGET', axis=1)
y_train_MCF_SMART=df_trans_MCF_SMART['TARGET']
scaler_MCF_SMART = StandardScaler()
X_scaled_MCF_SMART = scaler_MCF_SMART.fit_transform(X_MCF_SMART)
pca2_MCF_SMART = PCA(n_components=2)
X_pca2_MCF_SMART = pca2_MCF_SMART.fit_transform(X_scaled_MCF_SMART)
plt.figure(figsize=(12, 6))
plt.scatter(X_pca2_MCF_SMART[:,0][y_train_MCF_SMART==1], X_pca2_MCF_SMART[:,1][y_train_MCF_SMART==1], c='r')
plt.scatter(X_pca2_MCF_SMART[:,0][y_train_MCF_SMART==0], X_pca2_MCF_SMART[:,1][y_train_MCF_SMART==0], c='b')
plt.title("Clusterization PCA MCF_SMART")
plt.show()
'''% of EXPLAINED VARIANCE'''
pca_MCF_SMART = PCA().fit(X_scaled_MCF_SMART)
explained_variance_ratio_MCF_SMART = pca_MCF_SMART.explained_variance_ratio_
cumulative_variance_MCF_SMART = explained_variance_ratio_MCF_SMART.cumsum()
# Визуализация объясненной дисперсии
plt.plot(range(1, len(cumulative_variance_MCF_SMART) + 1), cumulative_variance_MCF_SMART, marker='o')
plt.xlabel("Number of components")
plt.ylabel("Cumulative proportion of Variance Explained")
plt.title("Analysis of explained variance MCF_SMART")
plt.show()
# TSNE
tsne_model_MCF_SMART = TSNE(n_components=2) #, perplexity=10
X_tsne_MCF_SMART = tsne_model_MCF_SMART.fit_transform(X_scaled_MCF_SMART)
# Visualization of results
plt.figure(figsize=(12, 6))
# plt.scatter(X_train_pca[:,0], X_train_pca[:,1], cmap='viridis')
plt.scatter(X_tsne_MCF_SMART[:,0][y_train_MCF_SMART==1], X_tsne_MCF_SMART[:,1][y_train_MCF_SMART==1], c='r')
plt.scatter(X_tsne_MCF_SMART[:,0][y_train_MCF_SMART==0], X_tsne_MCF_SMART[:,1][y_train_MCF_SMART==0], c='b')
plt.title("Clusterization TSNE MCF_SMART")
plt.show()
¶
2.2) HCC1806
¶
2.2.0) Importing datasets
hcc_unfiltered = pd.read_csv("SmartSeq\HCC1806_SmartS_Unfiltered_Data.txt",delimiter="\ ", engine='python',index_col=0)
print("UNFILTERED dimensions:", np.shape(hcc_unfiltered))
hcc_unfiltered_summary=hcc_unfiltered.describe()
hcc_unfiltered.describe()
UNFILTERED dimensions: (23396, 243)
| "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | ... | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 |
| mean | 99.565695 | 207.678278 | 9.694734 | 150.689007 | 35.700504 | 47.088434 | 152.799453 | 135.869422 | 38.363908 | 45.512139 | ... | 76.361771 | 105.566593 | 54.026116 | 29.763806 | 28.905411 | 104.740725 | 35.181569 | 108.197940 | 37.279962 | 76.303855 |
| std | 529.532443 | 981.107905 | 65.546050 | 976.936548 | 205.885369 | 545.367706 | 864.974182 | 870.729740 | 265.062493 | 366.704721 | ... | 346.659348 | 536.881574 | 344.068304 | 186.721266 | 135.474736 | 444.773045 | 170.872090 | 589.082268 | 181.398951 | 369.090274 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| 75% | 51.000000 | 125.000000 | 5.000000 | 40.000000 | 22.000000 | 17.000000 | 81.000000 | 76.000000 | 22.000000 | 18.000000 | ... | 56.000000 | 67.000000 | 29.000000 | 18.000000 | 19.000000 | 76.000000 | 24.000000 | 68.000000 | 22.000000 | 44.000000 |
| max | 35477.000000 | 69068.000000 | 6351.000000 | 70206.000000 | 17326.000000 | 47442.000000 | 43081.000000 | 62813.000000 | 30240.000000 | 35450.000000 | ... | 19629.000000 | 30987.000000 | 21894.000000 | 13457.000000 | 11488.000000 | 33462.000000 | 15403.000000 | 34478.000000 | 10921.000000 | 28532.000000 |
8 rows × 243 columns
hcc_filtered = pd.read_csv("SmartSeq\HCC1806_SmartS_Filtered_Data.txt",delimiter="\ ", engine='python',index_col=0)
print("FILTERED dimensions:", np.shape(hcc_filtered))
hcc_filtered_summary=hcc_filtered.describe()
hcc_filtered.describe()
FILTERED dimensions: (19503, 227)
| "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A9_Normoxia_S20_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | ... | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 | 19503.000000 |
| mean | 119.427883 | 249.107522 | 180.739527 | 42.818233 | 56.444393 | 183.264677 | 162.976670 | 46.014305 | 54.589961 | 96.803210 | ... | 91.583090 | 126.622930 | 64.801005 | 35.702302 | 34.670461 | 125.629544 | 42.195457 | 129.769010 | 44.715941 | 91.517561 |
| std | 577.934133 | 1069.768525 | 1067.470509 | 224.823960 | 596.882811 | 944.432350 | 951.367277 | 289.708746 | 401.024242 | 487.943421 | ... | 377.847391 | 585.760835 | 375.921207 | 203.991666 | 147.706909 | 484.448028 | 186.359651 | 643.033801 | 197.842998 | 402.529704 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 3.000000 | 7.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 4.000000 | 0.000000 | 0.000000 | 9.000000 | ... | 9.000000 | 4.000000 | 1.000000 | 2.000000 | 1.000000 | 15.000000 | 3.000000 | 4.000000 | 2.000000 | 8.000000 |
| 75% | 75.000000 | 179.000000 | 111.000000 | 31.000000 | 29.000000 | 126.000000 | 106.000000 | 32.000000 | 29.000000 | 78.000000 | ... | 77.000000 | 94.000000 | 42.000000 | 25.000000 | 27.000000 | 105.000000 | 34.000000 | 94.000000 | 32.000000 | 63.000000 |
| max | 35477.000000 | 69068.000000 | 70206.000000 | 17326.000000 | 47442.000000 | 43081.000000 | 62813.000000 | 30240.000000 | 35450.000000 | 42310.000000 | ... | 19629.000000 | 30987.000000 | 21894.000000 | 13457.000000 | 11488.000000 | 33462.000000 | 15403.000000 | 34478.000000 | 10921.000000 | 28532.000000 |
8 rows × 227 columns
hcc_removed_elements = hcc_unfiltered[~hcc_unfiltered.index.isin(hcc_filtered.index)]
columns_to_remove = hcc_filtered.columns.intersection(hcc_removed_elements.columns)
hcc_removed_elements = hcc_unfiltered.drop(columns=columns_to_remove)
hcc_removed_elements_summary=hcc_removed_elements.describe()
print("REMOVED DATA dimensions:", np.shape(hcc_removed_elements))
hcc_removed_elements.describe()
REMOVED DATA dimensions: (23396, 16)
| "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1B1_Hypoxia_S98_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1B4_Hypoxia_S105_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1D3_Hypoxia_S6_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1F8_Normoxia_S18_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1G6_Hypoxia_S15_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate2E12_Normoxia_S61_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate2F12_Normoxia_S62_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate2G10_Normoxia_S157_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate2H2_Hypoxia_S35_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate3D10_Normoxia_S188_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate3D11_Normoxia_S92_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate3D2_Hypoxia_S168_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate3D3_Hypoxia_S72_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate3D5_Hypoxia_S78_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4B1_Hypoxia_S221_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 |
| mean | 9.694734 | 0.019106 | 8.914515 | 149.442255 | 118.581980 | 3.163105 | 0.011840 | 0.004873 | 20.487434 | 246.116088 | 0.028937 | 0.007010 | 0.021799 | 0.014618 | 0.027740 | 9.328090 |
| std | 65.546050 | 1.003615 | 43.524520 | 426.779488 | 498.780729 | 25.101297 | 0.807311 | 0.125665 | 183.626052 | 1484.050714 | 2.331891 | 0.680409 | 1.538074 | 1.063188 | 1.334005 | 56.229778 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 14.000000 | 3.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 5.000000 | 0.000000 | 4.000000 | 142.000000 | 82.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 149.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| max | 6351.000000 | 140.000000 | 2047.000000 | 24384.000000 | 32060.000000 | 1304.000000 | 98.000000 | 14.000000 | 8437.000000 | 85472.000000 | 320.000000 | 103.000000 | 220.000000 | 160.000000 | 168.000000 | 4863.000000 |
¶
2.2.1) Statistical study of mean and STD on cells
We used same approach done on MCF7 dataset (2.1.1)
¶
2.2.1.1) Study of mean on cells
hcc_unfiltered_mean_max_value = hcc_unfiltered_summary.iloc[1].max()
print("Max value in unfiltered data:", hcc_unfiltered_mean_max_value)
hcc_unfiltered_mean_min_value = hcc_unfiltered_summary.iloc[1].min()
print("Min value in unfiltered data:", hcc_unfiltered_mean_min_value)
Max value in unfiltered data: 246.11608822020858 Min value in unfiltered data: 0.004872627799623867
sns.displot(hcc_unfiltered_summary.iloc[1], bins=50)
plt.xlim(0, 250)
plt.ylim(0, 20)
plt.title("Distribution of mean in unfiltered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In unfiltered dataset means are concentrated mostly around 0 and in the intervals [25, 60] and [70, 160]
hcc_filtered_mean_max_value = hcc_filtered_summary.iloc[1].max()
print("Max value in filtered data:", hcc_filtered_mean_max_value)
hcc_filtered_mean_min_value = hcc_filtered_summary.iloc[1].min()
print("Min value in filtered data:", hcc_filtered_mean_min_value)
Max value in filtered data: 249.10752191970465 Min value in filtered data: 17.54094241911501
sns.displot(hcc_filtered_summary.iloc[1], bins=50)
plt.xlim(0, 250)
plt.ylim(0,20)
plt.title("Distribution of mean in filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In filtered dataset, means are distributed mostly on interval [20, 200] with peaks around[30, 50], around 75, around 130 and around 160
hcc_removed_elements_mean_max_value = hcc_removed_elements_summary.iloc[1].max()
print("Max value in removed elements:", hcc_removed_elements_mean_max_value)
hcc_removed_elements_mean_min_value = hcc_removed_elements_summary.iloc[1].min()
print("Min value in removed elements:", hcc_removed_elements_mean_min_value)
Max value in removed elements: 246.11608822020858 Min value in removed elements: 0.004872627799623867
sns.displot(hcc_removed_elements_summary.iloc[1], bins=50)
plt.xlim(0, 250)
plt.ylim(0,10)
plt.title("Distribution of mean in removed elements")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In removed elements dataset almost all elements have mean around 0.
# is cell with highest mean value in filtered and unfiltered data the same?
max_mean_column_filtered = hcc_filtered.mean().idxmax()
max_mean_column_unfiltered = hcc_unfiltered.mean().idxmax()
print("is cell with highest mean value in filtered and unfiltered data the same?", max_mean_column_filtered==max_mean_column_unfiltered,
"\nmax mean column in filtered data:", max_mean_column_filtered, " and the value is:", hcc_filtered.mean().max(),
"\nmax mean column in unfiltered data:", max_mean_column_unfiltered, " and the value is:", hcc_unfiltered.mean().max())
is cell with highest mean value in filtered and unfiltered data the same? False max mean column in filtered data: "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" and the value is: 249.10752191970465 max mean column in unfiltered data: "output.STAR.PCRPlate2H2_Hypoxia_S35_Aligned.sortedByCoord.out.bam" and the value is: 246.11608822020858
# is cell with highest mean value in unfiltered contained in filtered?
print("is cell with highest mean value in unfiltered contained in filtered?")
if max_mean_column_unfiltered in hcc_filtered:
print("yes")
else:
print("no")
is cell with highest mean value in unfiltered contained in filtered? no
# is cell with lowest mean value in filtered and unfiltered data the same?
min_mean_column_filtered = hcc_filtered.mean().idxmin()
min_mean_column_unfiltered = hcc_unfiltered.mean().idxmin()
print("is cell with lowest mean value in filtered and unfiltered data the same?", min_mean_column_filtered==min_mean_column_unfiltered,
"\nmin mean column in filtered data:", min_mean_column_filtered, " and the value is:", hcc_filtered.mean().min(),
"\nmin mean column in unfiltered data:", min_mean_column_unfiltered, " and the value is:", hcc_unfiltered.mean().min())
is cell with lowest mean value in filtered and unfiltered data the same? False min mean column in filtered data: "output.STAR.PCRPlate3D1_Hypoxia_S161_Aligned.sortedByCoord.out.bam" and the value is: 17.54094241911501 min mean column in unfiltered data: "output.STAR.PCRPlate2F12_Normoxia_S62_Aligned.sortedByCoord.out.bam" and the value is: 0.004872627799623867
# is cell with lowest mean value in unfiltered contained in filtered?
print("is cell with lowest mean value in unfiltered contained in filtered?")
if min_mean_column_unfiltered in hcc_filtered:
print("yes")
else:
print("no")
is cell with lowest mean value in unfiltered contained in filtered? no
¶
2.2.1.2) Study of STD
hcc_unfiltered_std_max_value = hcc_unfiltered_summary.iloc[2].max()
print("max STD value in unfiltered:", hcc_unfiltered_std_max_value)
hcc_unfiltered_std_min_value = hcc_unfiltered_summary.iloc[2].min()
print("min TD value in unfiltered:", hcc_unfiltered_std_min_value)
max STD value in unfiltered: 1794.9847811738643 min TD value in unfiltered: 0.1256646395877129
sns.displot(hcc_unfiltered_summary.iloc[2], bins=100)
plt.xlim(0, 1750)
plt.ylim(0,15)
plt.title("Distribution of STD in unfiltered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
STD in unfiltered is distributed mainly around 0 and in the interval [150, 800] with some peaks around 180, 250, 450 and 600.
hcc_filtered_std_max_value = hcc_filtered_summary.iloc[2].max()
print("max STD value in filtered:", hcc_filtered_std_max_value)
hcc_filtered_std_min_value = hcc_filtered_summary.iloc[2].min()
print("min STD value in filtered:", hcc_unfiltered_std_min_value)
max STD value in filtered: 1964.1905749569585 min STD value in filtered: 0.1256646395877129
sns.displot(hcc_filtered_summary.iloc[2], bins=100)
plt.xlim(0, 1750)
plt.ylim(0,15)
plt.title("Distribution of STD in filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In filtered dataset STD is mostly distributed in interval [0, 800] with peaks in intervall [200, 300], and around 700.
hcc_removed_elements_std_max_value = hcc_removed_elements_summary.iloc[2].max()
print("max STD value in removed elements:", hcc_removed_elements_std_max_value)
hcc_removed_elements_std_min_value = hcc_removed_elements_summary.iloc[2].min()
print("min STD value in removed elements:", hcc_removed_elements_std_min_value)
max STD value in removed elements: 1484.0507137548668 min STD value in removed elements: 0.1256646395877129
sns.displot(hcc_removed_elements_summary.iloc[2], bins=100)
plt.xlim(0, 1750)
plt.ylim(0,10)
plt.title("Distribution of STD in removed elements")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Almost all elements in removed elements dataframe have STD equal to 0
# is cell with highest std value in filtered and unfiltered data the same?
max_std_column_filtered = hcc_filtered.std().idxmax()
max_std_column_unfiltered = hcc_unfiltered.std().idxmax()
print("is cell with max std value in filtered and unfiltered data the same?", max_std_column_filtered==max_std_column_unfiltered,
"\nmin std column in filtered data:", max_std_column_filtered, " and the value is:", hcc_filtered.std().max(),
"\nmin std column in unfiltered data:", max_std_column_unfiltered, " and the value is:", hcc_unfiltered.std().max())
is cell with max std value in filtered and unfiltered data the same? True min std column in filtered data: "output.STAR.PCRPlate1G8_Normoxia_S19_Aligned.sortedByCoord.out.bam" and the value is: 1964.1905749569585 min std column in unfiltered data: "output.STAR.PCRPlate1G8_Normoxia_S19_Aligned.sortedByCoord.out.bam" and the value is: 1794.9847811738641
# is cell with max std value in unfiltered contained in filtered?
print("is cell with max std value in unfiltered contained in filtered?")
if max_std_column_unfiltered in hcc_filtered:
print("yes")
else:
print("no")
is cell with max std value in unfiltered contained in filtered? yes
# is cell with lowest std value in filtered and unfiltered data the same?
min_std_column_filtered = hcc_filtered.std().idxmin()
min_std_column_unfiltered = hcc_unfiltered.std().idxmin()
print("is cell with lowest std value in filtered and unfiltered data the same?", min_std_column_filtered==min_std_column_unfiltered,
"\nmin std column in filtered data:", min_std_column_filtered, " and the value is:", hcc_filtered.std().min(),
"\nmin std column in unfiltered data:", min_std_column_unfiltered, " and the value is:", hcc_unfiltered.std().min())
is cell with lowest std value in filtered and unfiltered data the same? False min std column in filtered data: "output.STAR.PCRPlate3F3_Hypoxia_S170_Aligned.sortedByCoord.out.bam" and the value is: 88.62128925222184 min std column in unfiltered data: "output.STAR.PCRPlate2F12_Normoxia_S62_Aligned.sortedByCoord.out.bam" and the value is: 0.1256646395877129
# is cell with lowest std value in unfiltered contained in filtered?
print("is cell with lowest std value in unfiltered contained in filtered?")
if min_std_column_unfiltered in hcc_filtered:
print("yes")
else:
print("no")
is cell with lowest std value in unfiltered contained in filtered? no
¶
2.2.1.3) Details of distribution of mean
hcc_unfiltered_summary.iloc[1].describe()
count 243.000000 mean 86.010698 std 50.087964 min 0.004873 25% 42.360339 50% 88.376004 75% 125.029129 max 246.116088 Name: mean, dtype: float64
In the unfiltered dataset the mean value for means is 86.01, almost equal to the median value (88.37), suggesting that the distribution is symmetric.
Moreover, given the maximum value equal to 246 and minimum value 0, the expected 25% qualtile, 50% qualtile and 75 quantile in case of costant distribution function would have been 61.5, 123 and 184, while they are 42.4, 88.3 and 125, suggesting that the majority of datas have low mean and distribution is shifted toward 0.
hcc_filtered_summary.iloc[1].describe()
count 227.000000 mean 107.439569 std 55.590688 min 17.540942 25% 52.710173 50% 110.598728 75% 152.016254 max 249.107522 Name: mean, dtype: float64
In the filtered dataset the mean value for means is 107.4, and angain is close to median value (110.6), suggesting that the distribution is symmetric.
Moreover, given the maximum value equal to 249 and minimum value 10, the expected 25% qualtile, 50% qualtile and 75 quantile in case of costant distribution function would have been 69.75, 129.5 and 189.25, while they are 52, 110.3 and 152, suggesting again that the majority of datas have mean between the 25% quantile and the 50% quantile.
Furthermore, passing from funfiltered to filtered dataset, STD increases, so the distribution of the mean is more spread
hcc_removed_elements_summary.iloc[1].describe()
count 16.000000 mean 35.366508 std 71.946914 min 0.004873 25% 0.017984 50% 1.596021 75% 12.392909 max 246.116088 Name: mean, dtype: float64
In removed elements the mean is much larger than the median, saying that there are just few elements that have high mean while majority of elements have low mean.
Moreover, the 25% quantile (0.017984) is very close to minimum value (0.004873) and the 50% one is 1.596021, so, as further confirm of what stated before, vast majority of elements have low mean.
¶
2.2.1.4) Details of distribution of STD
hcc_unfiltered_summary.iloc[2].describe()
count 243.000000 mean 478.838710 std 324.107809 min 0.125665 25% 227.812122 50% 444.773045 75% 641.865658 max 1794.984781 Name: std, dtype: float64
hcc_filtered_summary.iloc[2].describe()
count 227.000000 mean 546.126224 std 338.318040 min 88.621289 25% 277.861442 50% 512.384830 75% 704.588843 max 1964.190575 Name: std, dtype: float64
Passing from unfiltered to filtered dataset mean of STD increases, implying that the distribution is more spread
hcc_removed_elements_summary.iloc[2].describe()
count 16.000000 mean 174.532674 std 382.069532 min 0.125665 25% 1.048295 50% 13.716594 75% 95.066050 max 1484.050714 Name: std, dtype: float64
¶
2.2.2) Statistical study of mean and STD on genes
We applied same technique done before (see 2.1.2)
¶
2.2.2.1) Study of mean
hcc_unfiltered_transposed = hcc_unfiltered.T
print("UNFILTERED TRANSPOSED dimensions:", np.shape(hcc_unfiltered_transposed))
hcc_unfiltered_transposed_summary=hcc_unfiltered_transposed.describe()
hcc_unfiltered_transposed.describe()
UNFILTERED TRANSPOSED dimensions: (243, 23396)
| "WASH7P" | "CICP27" | "DDX11L17" | "WASH9P" | "OR4F29" | "MTND1P23" | "MTND2P28" | "MTCO1P12" | "MTCO2P12" | "MTATP8P1" | ... | "MT-TH" | "MT-TS2" | "MT-TL2" | "MT-ND5" | "MT-ND6" | "MT-TE" | "MT-CYB" | "MT-TT" | "MT-TP" | "MAFIP" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | ... | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 |
| mean | 0.045267 | 0.119342 | 0.469136 | 0.255144 | 0.127572 | 117.930041 | 28.427984 | 904.308642 | 1.403292 | 0.378601 | ... | 10.358025 | 5.930041 | 13.493827 | 2290.213992 | 386.901235 | 18.246914 | 2163.588477 | 20.613169 | 46.444444 | 3.897119 |
| std | 0.318195 | 0.594531 | 1.455282 | 0.818639 | 0.440910 | 103.038022 | 26.062662 | 654.520308 | 1.735003 | 0.747361 | ... | 10.910199 | 7.040559 | 11.803967 | 1726.423259 | 309.276105 | 54.076514 | 1730.393947 | 22.224590 | 47.684223 | 4.736193 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 46.500000 | 8.000000 | 390.500000 | 0.000000 | 0.000000 | ... | 2.000000 | 1.000000 | 4.000000 | 918.000000 | 138.500000 | 4.000000 | 947.500000 | 5.000000 | 14.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 94.000000 | 22.000000 | 790.000000 | 1.000000 | 0.000000 | ... | 7.000000 | 4.000000 | 10.000000 | 1848.000000 | 320.000000 | 11.000000 | 1774.000000 | 14.000000 | 38.000000 | 2.000000 |
| 75% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 157.000000 | 42.000000 | 1208.000000 | 2.000000 | 1.000000 | ... | 14.000000 | 7.500000 | 20.000000 | 3172.000000 | 528.000000 | 20.000000 | 2927.000000 | 30.500000 | 64.500000 | 6.000000 |
| max | 3.000000 | 5.000000 | 12.000000 | 6.000000 | 4.000000 | 694.000000 | 120.000000 | 3569.000000 | 12.000000 | 4.000000 | ... | 52.000000 | 43.000000 | 57.000000 | 8972.000000 | 1439.000000 | 804.000000 | 11383.000000 | 154.000000 | 409.000000 | 24.000000 |
8 rows × 23396 columns
hcc_filtered_transposed = hcc_filtered.T
print("FILTERED TRANSPOSED dimensions:", np.shape(hcc_filtered_transposed))
hcc_filtered_transposed_summary=hcc_filtered_transposed.describe()
hcc_filtered_transposed.describe()
FILTERED TRANSPOSED dimensions: (227, 19503)
| "CICP27" | "DDX11L17" | "WASH9P" | "OR4F29" | "MTND1P23" | "MTND2P28" | "MTCO1P12" | "MTCO2P12" | "MTATP8P1" | "MTATP6P1" | ... | "MT-TH" | "MT-TS2" | "MT-TL2" | "MT-ND5" | "MT-ND6" | "MT-TE" | "MT-CYB" | "MT-TT" | "MT-TP" | "MAFIP" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 227.000000 | 227.000000 | 227.00000 | 227.000000 | 227.000000 | 227.000000 | 227.000000 | 227.000000 | 227.000000 | 227.000000 | ... | 227.000000 | 227.000000 | 227.000000 | 227.000000 | 227.000000 | 227.000000 | 227.000000 | 227.000000 | 227.000000 | 227.000000 |
| mean | 0.101322 | 0.488987 | 0.23348 | 0.123348 | 121.867841 | 30.035242 | 952.537445 | 1.453744 | 0.405286 | 532.171806 | ... | 10.691630 | 6.193833 | 14.281938 | 2415.079295 | 406.603524 | 15.665198 | 2268.911894 | 21.682819 | 47.400881 | 4.057269 |
| std | 0.574801 | 1.491454 | 0.74259 | 0.443968 | 102.526406 | 25.907060 | 635.213609 | 1.747646 | 0.766308 | 387.720513 | ... | 10.744501 | 6.993509 | 11.758626 | 1690.605545 | 304.982898 | 19.386675 | 1700.731948 | 22.411593 | 41.691796 | 4.769119 |
| min | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 1.000000 | 0.000000 | 5.000000 | 0.000000 | 0.000000 | 4.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 18.000000 | 5.000000 | 0.000000 | 83.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 49.500000 | 9.000000 | 473.500000 | 0.000000 | 0.000000 | 238.500000 | ... | 3.000000 | 1.000000 | 5.000000 | 1067.500000 | 163.000000 | 4.000000 | 1008.500000 | 6.000000 | 16.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 99.000000 | 25.000000 | 826.000000 | 1.000000 | 0.000000 | 466.000000 | ... | 8.000000 | 4.000000 | 11.000000 | 2002.000000 | 329.000000 | 11.000000 | 1837.000000 | 15.000000 | 38.000000 | 2.000000 |
| 75% | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 158.000000 | 42.500000 | 1247.500000 | 2.000000 | 1.000000 | 696.000000 | ... | 14.000000 | 8.000000 | 21.000000 | 3324.500000 | 549.500000 | 20.500000 | 3046.000000 | 31.000000 | 65.500000 | 6.500000 |
| max | 5.000000 | 12.000000 | 6.00000 | 4.000000 | 694.000000 | 120.000000 | 3569.000000 | 12.000000 | 4.000000 | 2358.000000 | ... | 52.000000 | 43.000000 | 57.000000 | 8972.000000 | 1439.000000 | 212.000000 | 11383.000000 | 154.000000 | 289.000000 | 24.000000 |
8 rows × 19503 columns
hcc_removed_elements_transposed=hcc_removed_elements.T
print("REMOVED DATA dimensions:", np.shape(hcc_removed_elements_transposed))
hcc_removed_elements_transposed_summary=hcc_removed_elements_transposed.describe()
hcc_removed_elements_transposed.describe()
REMOVED DATA dimensions: (16, 23396)
| "WASH7P" | "CICP27" | "DDX11L17" | "WASH9P" | "OR4F29" | "MTND1P23" | "MTND2P28" | "MTCO1P12" | "MTCO2P12" | "MTATP8P1" | ... | "MT-TH" | "MT-TS2" | "MT-TL2" | "MT-ND5" | "MT-ND6" | "MT-TE" | "MT-CYB" | "MT-TT" | "MT-TP" | "MAFIP" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 16.000 | 16.000000 | 16.0000 | 16.000000 | 16.000000 | 16.000000 | 16.000000 | 16.000000 | 16.000000 | 16.0 | ... | 16.000000 | 16.000000 | 16.000000 | 16.000000 | 16.000000 | 16.000000 | 16.000000 | 16.000000 | 16.000000 | 16.000000 |
| mean | 0.125 | 0.375000 | 0.1875 | 0.562500 | 0.187500 | 62.062500 | 5.625000 | 220.062500 | 0.687500 | 0.0 | ... | 5.625000 | 2.187500 | 2.312500 | 518.687500 | 107.375000 | 54.875000 | 669.312500 | 5.437500 | 32.875000 | 1.625000 |
| std | 0.500 | 0.806226 | 0.7500 | 1.547848 | 0.403113 | 96.670898 | 15.894968 | 545.452286 | 1.400893 | 0.0 | ... | 12.467959 | 6.843184 | 4.686417 | 1199.906647 | 229.222708 | 199.958954 | 1475.697788 | 11.752837 | 101.412606 | 3.649201 |
| min | 0.000 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 3.000000 | 0.000000 | 4.500000 | 0.000000 | 0.0 | ... | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 6.000000 | 0.000000 | 12.500000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 0.000 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 115.000000 | 2.250000 | 85.750000 | 1.000000 | 0.0 | ... | 1.000000 | 0.000000 | 2.250000 | 234.500000 | 50.500000 | 8.250000 | 445.750000 | 2.500000 | 14.000000 | 0.250000 |
| max | 2.000 | 2.000000 | 3.0000 | 6.000000 | 1.000000 | 282.000000 | 64.000000 | 2073.000000 | 5.000000 | 0.0 | ... | 43.000000 | 27.000000 | 18.000000 | 4018.000000 | 741.000000 | 804.000000 | 5544.000000 | 40.000000 | 409.000000 | 12.000000 |
8 rows × 23396 columns
hcc_unfiltered_transposed_mean_max_value = hcc_unfiltered_transposed_summary.iloc[1].max()
print("Max mean value in unfiltered transposed data:", hcc_unfiltered_transposed_mean_max_value)
hcc_unfiltered_transposed_mean_min_value = hcc_unfiltered_transposed_summary.iloc[1].min()
print("Min mean value in unfiltered transposed data:", hcc_unfiltered_transposed_mean_min_value)
Max mean value in unfiltered transposed data: 27087.0 Min mean value in unfiltered transposed data: 0.00823045267489712
sns.displot(hcc_unfiltered_transposed_summary.iloc[1], bins=500)
plt.xlim(0, 27000)
plt.ylim(0, 1500)
plt.title("Distribution of mean in unfiltered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Histograms suggests that almost all elements have mean close to 0
hcc_filtered_transposed_mean_max_value = hcc_filtered_transposed_summary.iloc[1].max()
print("Max mean value in filtered transposed data:", hcc_filtered_transposed_mean_max_value)
hcc_filtered_transposed_mean_min_value = hcc_filtered_transposed_summary.iloc[1].min()
print("Min mean value in filtered transposed data:", hcc_filtered_transposed_mean_min_value)
Max mean value in filtered transposed data: 28423.713656387667 Min mean value in filtered transposed data: 0.02643171806167401
sns.displot(hcc_filtered_transposed_summary.iloc[1], bins=500)
plt.xlim(0, 27000)
plt.ylim(0, 1500)
plt.title("Distribution of mean in filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Histograms suggests that almost all elements have mean close to 0
hcc_removed_elements_transposed_mean_max_value = hcc_removed_elements_transposed_summary.iloc[1].max()
print("Max value in removed elements transposed data:", hcc_removed_elements_transposed_mean_max_value)
hcc_removed_elements_transposed_mean_min_value = hcc_removed_elements_transposed_summary.iloc[1].min()
print("Min value in removed elements transposed data:", hcc_removed_elements_transposed_mean_min_value)
Max value in removed elements transposed data: 8842.5 Min value in removed elements transposed data: 0.0
sns.displot(hcc_removed_elements_transposed_summary.iloc[1], bins=500)
plt.xlim(0, 6100)
plt.ylim(0, 1500)
plt.title("Distribution of mean in removed elements")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Histograms suggests that almost all elements have mean close to 0
# is cell with max mean value in filtered_tranposed and unfiltered_transposed data the same?
max_mean_column_filtered_transposed = hcc_filtered_transposed.mean().idxmin()
max_mean_column_unfiltered_transposed = hcc_unfiltered_transposed.mean().idxmin()
print("is cell with max mean value in filtered transposed and unfiltered transposed data the same?", max_mean_column_filtered_transposed==max_mean_column_unfiltered_transposed,
"\nmax mean column in filtered data:", max_mean_column_filtered_transposed, " and the value is:", hcc_filtered_transposed.mean().max(),
"\nmax mean column in unfiltered data:", max_mean_column_unfiltered_transposed, " and the value is:", hcc_unfiltered_transposed.mean().max())
is cell with max mean value in filtered transposed and unfiltered transposed data the same? False max mean column in filtered data: "EFCAB14P1" and the value is: 28423.713656387667 max mean column in unfiltered data: "CICP3" and the value is: 27087.0
# is cell with highest mean value in unfiltered contained in filtered?
if max_mean_column_unfiltered_transposed in hcc_filtered_transposed:
print("yes")
else:
print("no")
no
# is cell with lowest mean value in filtered_tranposed and unfiltered_transposed data the same?
min_mean_column_filtered_transposed = hcc_filtered_transposed.mean().idxmin()
min_mean_column_unfiltered_transposed = hcc_unfiltered_transposed.mean().idxmin()
print("is cell with lowest mean value in filtered transposed and unfiltered transposed data the same?", min_mean_column_filtered_transposed==min_mean_column_unfiltered_transposed,
"\nmin mean column in filtered data:", min_mean_column_filtered_transposed, " and the value is:", hcc_filtered_transposed.mean().min(),
"\nmin mean column in unfiltered data:", min_mean_column_unfiltered_transposed, " and the value is:", hcc_unfiltered_transposed.mean().min())
is cell with lowest mean value in filtered transposed and unfiltered transposed data the same? False min mean column in filtered data: "EFCAB14P1" and the value is: 0.02643171806167401 min mean column in unfiltered data: "CICP3" and the value is: 0.00823045267489712
# is cell with lowest mean value in unfiltered contained in filtered?
if min_mean_column_unfiltered_transposed in hcc_filtered_transposed:
print("yes")
else:
print("no")
no
¶
2.2.2.2) Study of STD
hcc_unfiltered_transposed_std_max_value = hcc_unfiltered_transposed_summary.iloc[2].max()
print("Max std value in unfiltered transposed data:", hcc_unfiltered_transposed_std_max_value)
hcc_unfiltered_transposed_std_min_value = hcc_unfiltered_transposed_summary.iloc[2].min()
print("Min std value in unfiltered transposed data:", hcc_unfiltered_transposed_std_min_value)
Max std value in unfiltered transposed data: 24669.076098596488 Min std value in unfiltered transposed data: 0.0905342064629544
sns.displot(hcc_unfiltered_transposed_summary.iloc[2], bins=500)
plt.xlim(0, 27200)
plt.ylim(0, 1500)
plt.title("Distribution of STD in unfiltered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Histograms suggests that almost all elements have STD close to 0
hcc_filtered_transposed_std_max_value = hcc_filtered_transposed_summary.iloc[2].max()
print("Max std value in filtered transposed data:", hcc_filtered_transposed_mean_max_value)
hcc_filtered_transposed_std_min_value = hcc_filtered_transposed_summary.iloc[2].min()
print("Min std value in filtered transposed data:", hcc_filtered_transposed_mean_min_value)
Max std value in filtered transposed data: 28423.713656387667 Min std value in filtered transposed data: 0.02643171806167401
sns.displot(hcc_filtered_transposed_summary.iloc[2], bins=1000)
plt.xlim(0, 27000)
plt.ylim(0, 1000)
plt.title("Distribution of STD in filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Histograms suggests that almost all elements have STD close to 0
hcc_removed_elements_transposed_std_max_value = hcc_removed_elements_transposed_summary.iloc[2].max()
print("Max value std in removed elements transposed data:", hcc_removed_elements_transposed_mean_max_value)
hcc_removed_elements_transposed_mean_min_value = hcc_removed_elements_transposed_summary.iloc[2].min()
print("Min value std in removed elements transposed data:", hcc_removed_elements_transposed_mean_min_value)
Max value std in removed elements transposed data: 8842.5 Min value std in removed elements transposed data: 0.0
sns.displot(hcc_removed_elements_transposed_summary.iloc[2], bins=1000)
plt.xlim(0, 6100)
plt.ylim(0, 3000)
plt.title("Distribution of STD in removed elements")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Histograms suggests that almost all elements have STD close to 0
# is cell with max mean value in filtered_tranposed and unfiltered_transposed data the same?
max_std_column_filtered_transposed = hcc_filtered_transposed.std().idxmin()
max_std_column_unfiltered_transposed = hcc_unfiltered_transposed.std().idxmin()
print("is cell with max std value in filtered transposed and unfiltered transposed data the same?", max_std_column_filtered_transposed==max_std_column_unfiltered_transposed,
"\nmax std column in filtered data:", max_std_column_filtered_transposed, " and the value is:", hcc_filtered_transposed.std().max(),
"\nmax std column in unfiltered data:", max_std_column_unfiltered_transposed, " and the value is:", hcc_unfiltered_transposed.std().max())
is cell with max std value in filtered transposed and unfiltered transposed data the same? False max std column in filtered data: "MIR6766" and the value is: 24582.568596498753 max std column in unfiltered data: "SBF1P2" and the value is: 24669.076098596484
# is cell with highest mean value in unfiltered contained in filtered?
if max_std_column_unfiltered_transposed in hcc_filtered_transposed:
print("yes")
else:
print("no")
no
# is cell with lowest std value in filtered_tranposed and unfiltered_transposed data the same?
min_std_column_filtered_transposed = hcc_filtered_transposed.std().idxmin()
min_std_column_unfiltered_transposed = hcc_unfiltered_transposed.std().idxmin()
print("is cell with lowest std value in filtered transposed and unfiltered transposed data the same?", min_std_column_filtered_transposed==min_std_column_unfiltered_transposed,
"\nmin std column in filtered data:", min_std_column_filtered_transposed, " and the value is:", hcc_filtered_transposed.std().min(),
"\nmin std column in unfiltered data:", min_std_column_unfiltered_transposed, " and the value is:", hcc_unfiltered_transposed.std().min())
is cell with lowest std value in filtered transposed and unfiltered transposed data the same? False min std column in filtered data: "MIR6766" and the value is: 0.1607698527031286 min std column in unfiltered data: "SBF1P2" and the value is: 0.0905342064629544
# is cell with lowest mean value in unfiltered contained in filtered?
if min_std_column_unfiltered_transposed in hcc_filtered_transposed:
print("yes")
else:
print("no")
no
¶
2.2.2.3) Details on distribution of mean
hcc_unfiltered_transposed_summary.iloc[1].describe()
count 23396.000000 mean 86.010698 std 421.536457 min 0.008230 25% 0.279835 50% 7.187243 75% 60.075103 max 27087.000000 Name: mean, dtype: float64
All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one
hcc_filtered_transposed_summary.iloc[1].describe()
count 19503.000000 mean 107.439569 std 480.983441 min 0.026432 25% 1.303965 50% 17.405286 75% 83.696035 max 28423.713656 Name: mean, dtype: float64
All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one
hcc_removed_elements_transposed_summary.iloc[1].describe()
count 23396.000000 mean 35.366508 std 151.701045 min 0.000000 25% 0.062500 50% 3.562500 75% 29.000000 max 8842.500000 Name: mean, dtype: float64
All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one
¶
2.2.2.4) Details of distribution of STD
hcc_unfiltered_transposed_summary.iloc[2].describe()
count 23396.000000 mean 87.586074 std 389.473821 min 0.090534 25% 1.590075 50% 18.227290 75% 77.864077 max 24669.076099 Name: std, dtype: float64
All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one
hcc_filtered_transposed_summary.iloc[2].describe()
count 19503.000000 mean 102.497791 std 417.430333 min 0.160770 25% 4.977498 50% 31.510106 75% 94.310478 max 24582.568596 Name: std, dtype: float64
All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one
hcc_removed_elements_transposed_summary.iloc[2].describe()
count 23396.000000 mean 85.593360 std 389.579492 min 0.000000 25% 0.250000 50% 10.955876 75% 72.964289 max 21782.276646 Name: std, dtype: float64
All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one
¶
2.2.3) TRIAL AND ERROR OF PSEUDO FILTERING ON CELLS
We used same approach applied before (see 2.1.3)
# similarity between filtered and unfiltred data
filtered_set = set(hcc_filtered.columns)
unfiltered_set = set(hcc_unfiltered.columns)
intersection = filtered_set.intersection(unfiltered_set)
union = filtered_set.union(unfiltered_set)
similarity = len(intersection) / len(union)
removed = unfiltered_set - filtered_set
print(f"Similarity Percentage: {similarity}%", "\nnumber of removed:", len(removed))
Similarity Percentage: 0.934156378600823% number of removed: 16
# trial 1: filter removing cells with mean below 14 and std below 60
# Filter columns based on mean and standard deviation
filtered_columns = hcc_unfiltered.columns[(hcc_unfiltered.mean() >= 14) & (hcc_unfiltered.std() >= 81)]
# Create pseudo_filtered dataset
pseudo_filtered = hcc_unfiltered[filtered_columns]
# mean over 14: 98.26839826839827
# mean over 15: Similarity Percentage: 97.83549783549783%
pseudo_filtered_summary=pseudo_filtered.describe()
pseudo_filtered.describe()
| "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A9_Normoxia_S20_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | ... | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 |
| mean | 99.565695 | 207.678278 | 150.689007 | 35.700504 | 47.088434 | 152.799453 | 135.869422 | 38.363908 | 45.512139 | 80.726064 | ... | 76.361771 | 105.566593 | 54.026116 | 29.763806 | 28.905411 | 104.740725 | 35.181569 | 108.197940 | 37.279962 | 76.303855 |
| std | 529.532443 | 981.107905 | 976.936548 | 205.885369 | 545.367706 | 864.974182 | 870.729740 | 265.062493 | 366.704721 | 446.951678 | ... | 346.659348 | 536.881574 | 344.068304 | 186.721266 | 135.474736 | 444.773045 | 170.872090 | 589.082268 | 181.398951 | 369.090274 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | ... | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| 75% | 51.000000 | 125.000000 | 40.000000 | 22.000000 | 17.000000 | 81.000000 | 76.000000 | 22.000000 | 18.000000 | 57.000000 | ... | 56.000000 | 67.000000 | 29.000000 | 18.000000 | 19.000000 | 76.000000 | 24.000000 | 68.000000 | 22.000000 | 44.000000 |
| max | 35477.000000 | 69068.000000 | 70206.000000 | 17326.000000 | 47442.000000 | 43081.000000 | 62813.000000 | 30240.000000 | 35450.000000 | 42310.000000 | ... | 19629.000000 | 30987.000000 | 21894.000000 | 13457.000000 | 11488.000000 | 33462.000000 | 15403.000000 | 34478.000000 | 10921.000000 | 28532.000000 |
8 rows × 231 columns
pseudo_filtered_mean_max_value = pseudo_filtered_summary.iloc[1].max()
print("max mean value in pseudo filtered:", pseudo_filtered_mean_max_value)
hcc_filtered_mean_min_value = pseudo_filtered_summary.iloc[1].min()
print("min mean value in pseudo filtered:", hcc_filtered_mean_min_value)
max mean value in pseudo filtered: 246.11608822020858 min mean value in pseudo filtered: 14.624807659428962
sns.displot(pseudo_filtered_summary.iloc[1], bins=100)
plt.xlim(0, 105)
plt.ylim(0, 30)
plt.title("Distribution of means in pseudo filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
pseudo_filtered_std_max_value = pseudo_filtered_summary.iloc[2].max()
print("max STD value in pseudo filtered:", pseudo_filtered_std_max_value)
hcc_filtered_std_min_value = pseudo_filtered_summary.iloc[2].min()
print("min STD value in pseudo filtered:", hcc_filtered_std_min_value)
max STD value in pseudo filtered: 1794.9847811738643 min STD value in pseudo filtered: 81.56511437149355
sns.displot(pseudo_filtered_summary.iloc[2], bins=100)
plt.xlim(0, 1700)
plt.ylim(0, 20)
plt.title("Distribution of STD in pseudo filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
# similarity between filtered and pseudo_filtered data
# Convert the dataframes to sets
filtered_set = set(hcc_filtered.columns)
pseudo_set = set(pseudo_filtered.columns)
unfiltered_set = set(hcc_unfiltered.columns)
# Calculate the intersection and union of the sets
intersection = filtered_set.intersection(pseudo_set)
union = filtered_set.union(pseudo_set)
# Calculate the Jaccard similarity coefficient
similarity = len(intersection) / len(union)
# Convert the similarity to percentage
similarity_percentage = similarity * 100
rem=unfiltered_set - pseudo_set
diff = pseudo_set - filtered_set #elements in pseudo_filtered that are not in filtered
diff1 = filtered_set - pseudo_set #elements in filtered that are not in pseudo_filtered
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage}%", "\nnumber of removed", len(rem),
"\nnumber of elements in pseudo_filtered that are not in filtered:", len(diff),
"\nnumber of elemtns in filtered that are not in pseudo_filtered:", len(diff1))
Similarity Percentage: 98.26839826839827% number of removed 12 number of elements in pseudo_filtered that are not in filtered: 4 number of elemtns in filtered that are not in pseudo_filtered: 0
Check whether mean=14 and STD=60 are good lower bounds
#check 1: reduce mean to 8 and std to 60
# Filter columns based on mean and standard deviation
filtered_columns_check1 = hcc_unfiltered.columns[(hcc_unfiltered.mean() >= 8) & (hcc_unfiltered.std() >= 60)]
# Create pseudo_filtered dataset
pseudo_filtered_check1 = hcc_unfiltered[filtered_columns_check1]
pseudo_filtered_check1_summary=pseudo_filtered_check1.describe()
pseudo_filtered_check1.describe()
# similarity between filtered and _check data
# Convert the dataframes to sets
filtered_set1 = set(hcc_filtered.columns)
pseudo_set_check1 = set(pseudo_filtered_check1.columns)
unfiltered_set1 = set(hcc_unfiltered.columns)
# Calculate the intersection and union of the sets
intersection_check1 = filtered_set1.intersection(pseudo_set_check1)
union_check1 = filtered_set1.union(pseudo_set_check1)
# Calculate the Jaccard similarity coefficient
similarity_check1 = len(intersection_check1) / len(union_check1)
# Convert the similarity to percentage
similarity_percentage_check1 = similarity_check1 * 100
rem_check1=unfiltered_set - pseudo_set_check1
diff__check1 = pseudo_set_check1 - filtered_set #elements in _check that are not in filtered
diff1__check1 = filtered_set - pseudo_set_check1 #elements in filtered that are not in _check
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage_check1}%", "\nnumber of removed", len(rem_check1), "\nnumber of elements in _check that are not in filtered:", len(diff__check1), "\nnumber of elemtns in filtered that are not in _check:", len(diff1__check1))
# I removed 66 elements. 7 of these are mistakes. still need to remove 11 elements
Similarity Percentage: 97.84482758620689% number of removed 11 number of elements in _check that are not in filtered: 5 number of elemtns in filtered that are not in _check: 0
#check 3: increase mean to 16 & std to 100
# Filter columns based on mean and standard deviation
filtered_columns_check3 = hcc_unfiltered.columns[(hcc_unfiltered.mean() >= 16) & (hcc_unfiltered.std() >= 100)]
# Create pseudo_filtered dataset
pseudo_filtered_check3 = hcc_unfiltered[filtered_columns_check3]
pseudo_filtered_check3_summary=pseudo_filtered_check3.describe()
# similarity between filtered and _check data
# Convert the dataframes to sets
filtered_set = set(hcc_filtered.columns)
pseudo_set_check3 = set(pseudo_filtered_check3.columns)
unfiltered_set = set(hcc_unfiltered.columns)
# Calculate the intersection and union of the sets
intersection_check3 = filtered_set.intersection(pseudo_set_check3)
union_check3 = filtered_set.union(pseudo_set_check3)
# Calculate the Jaccard similarity coefficient
similarity_check3 = len(intersection_check3) / len(union_check3)
# Convert the similarity to percentage
similarity_percentage_check3 = similarity_check3 * 100
rem_check3=unfiltered_set - pseudo_set_check3
diff__check3 = pseudo_set - filtered_set #elements in _check that are not in filtered
diff1__check3 = filtered_set - pseudo_set_check3 #elements in filtered that are not in _check
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage_check3}%", "\nnumber of removed", len(rem_check3),
"\nnumber of elements in _check that are not in filtered:", len(diff__check3),
"\nnumber of elemtns in filtered that are not in _check:", len(diff1__check3))
# I removed 66 elements. 7 of these are mistakes. still need to remove 11 elements
Similarity Percentage: 96.96969696969697% number of removed 15 number of elements in _check that are not in filtered: 4 number of elemtns in filtered that are not in _check: 3
THUS MEAN=14 and STD=82 are best LOWER BOUNDS
# trial 2: remove elements with mean over 210
# since element with max std in unfiltered is also in filtered, I will not put an upper bound on std
# Filter columns based on mean and standard deviation
filtered_columns2 = pseudo_filtered.columns[(pseudo_filtered.mean() < 210)]
# Create pseudo_filtered dataset
pseudo_filtered2= hcc_unfiltered[filtered_columns2]
pseudo_filtered2_summary=pseudo_filtered2.describe()
pseudo_filtered2.describe()
| "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A9_Normoxia_S20_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | ... | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 | 23396.000000 |
| mean | 99.565695 | 207.678278 | 150.689007 | 35.700504 | 47.088434 | 152.799453 | 135.869422 | 38.363908 | 45.512139 | 80.726064 | ... | 76.361771 | 105.566593 | 54.026116 | 29.763806 | 28.905411 | 104.740725 | 35.181569 | 108.197940 | 37.279962 | 76.303855 |
| std | 529.532443 | 981.107905 | 976.936548 | 205.885369 | 545.367706 | 864.974182 | 870.729740 | 265.062493 | 366.704721 | 446.951678 | ... | 346.659348 | 536.881574 | 344.068304 | 186.721266 | 135.474736 | 444.773045 | 170.872090 | 589.082268 | 181.398951 | 369.090274 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | ... | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| 75% | 51.000000 | 125.000000 | 40.000000 | 22.000000 | 17.000000 | 81.000000 | 76.000000 | 22.000000 | 18.000000 | 57.000000 | ... | 56.000000 | 67.000000 | 29.000000 | 18.000000 | 19.000000 | 76.000000 | 24.000000 | 68.000000 | 22.000000 | 44.000000 |
| max | 35477.000000 | 69068.000000 | 70206.000000 | 17326.000000 | 47442.000000 | 43081.000000 | 62813.000000 | 30240.000000 | 35450.000000 | 42310.000000 | ... | 19629.000000 | 30987.000000 | 21894.000000 | 13457.000000 | 11488.000000 | 33462.000000 | 15403.000000 | 34478.000000 | 10921.000000 | 28532.000000 |
8 rows × 230 columns
pseudo_filtered2_mean_max_value = pseudo_filtered2_summary.iloc[1].max()
print("max mean value in pseudo filtered2:", pseudo_filtered2_mean_max_value)
hcc_filtered2_mean_min_value = pseudo_filtered2_summary.iloc[1].min()
print("min mean value in pseudo filtered2:", hcc_filtered2_mean_min_value)
max mean value in pseudo filtered2: 207.67827833817748 min mean value in pseudo filtered2: 14.624807659428962
sns.displot(pseudo_filtered2_summary.iloc[1], bins=100)
plt.xlim(0, 105)
plt.ylim(0, 30)
plt.title("Distribution of means in pseudo filtered2")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
pseudo_filtered2_std_max_value = pseudo_filtered2_summary.iloc[2].max()
print("max std value in pseudo filtered2:", pseudo_filtered2_std_max_value)
hcc_filtered2_std_min_value = pseudo_filtered2_summary.iloc[2].min()
print("min std value in pseudo filtered2:", hcc_filtered2_std_min_value)
max std value in pseudo filtered2: 1794.9847811738643 min std value in pseudo filtered2: 81.56511437149355
sns.displot(pseudo_filtered2_summary.iloc[2], bins=100)
plt.xlim(0, 1700)
plt.ylim(0, 20)
plt.title("Distribution of STD in pseudo filtered2")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
# similarity between filtered and pseudo_filtered2 data
# Convert the dataframes to sets
filtered_set = set(hcc_filtered.columns)
pseudo_set2 = set(pseudo_filtered2.columns)
unfiltered_set = set(hcc_unfiltered.columns)
# Calculate the intersection and union of the sets
intersection2 = filtered_set.intersection(pseudo_set2)
union2 = filtered_set.union(pseudo_set2)
# Calculate the Jaccard similarity coefficient
similarity2 = len(intersection2) / len(union2)
# Convert the similarity to percentage
similarity_percentage2 = similarity2 * 100
rem2=unfiltered_set - pseudo_set2
diff_2 = pseudo_set2 - filtered_set #elements in pseudo_filtered3 that are not in filtered
diff1_2 = filtered_set - pseudo_set2 # in filtered that are not in pseudo_filtered3
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage2}%", "\nnumber of removed", len(rem2),
"\nnumber of elements in pseudo_filtered2 that are not in filtered:", len(diff_2),
"\nnumber of elemtns in filtered that are not in pseudo_filtered2:", len(diff1_2))
# I removed 66 elements. 7 of these are mistakes. still need to remove 11 elements
98.26839826839827
Similarity Percentage: 98.69565217391305% number of removed 13 number of elements in pseudo_filtered2 that are not in filtered: 3 number of elemtns in filtered that are not in pseudo_filtered2: 0
98.26839826839827
Check if mean=210 is a good upper bound
# check 1: reduce mean to 207
# NOTE: with this upper bound does I remove 1 elemnent and mean does not change. Thus do checks using higher upper bounds may not help
# Filter columns based on mean and standard deviation
filtered_columns4 = pseudo_filtered2.columns[(pseudo_filtered2.mean() < 207)]
# Create pseudo_filtered dataset
pseudo_filtered4 = hcc_unfiltered[filtered_columns4]
pseudo_filtered4_summary=pseudo_filtered4.describe()
# similarity between filtered and pseudo_filtered4 data
# Convert the dataframes to sets
filtered_set = set(hcc_filtered.columns)
pseudo_set4 = set(pseudo_filtered4.columns)
unfiltered_set = set(hcc_unfiltered.columns)
# Calculate the intersection and union of the sets
intersection4 = filtered_set.intersection(pseudo_set4)
union4 = filtered_set.union(pseudo_set4)
# Calculate the Jaccard similarity coefficient
similarity4 = len(intersection4) / len(union4)
# Convert the similarity to percentage
similarity_percentage4 = similarity4 * 100
rem4=unfiltered_set - pseudo_set4
diff_4 = pseudo_set4 - filtered_set #elements in pseudo_filtered3 that are not in filtered
diff1_4 = filtered_set - pseudo_set4 #elements in filtered that are not in pseudo_filtered3
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage4}%", "\nnumber of removed", len(rem4),
"\nnumber of elements in pseudo_filtered2 that are not in filtered:", len(diff_4),
"\nnumber of elemtns in filtered that are not in pseudo_filtered2:", len(diff1_4))
Similarity Percentage: 97.82608695652173% number of removed 15 number of elements in pseudo_filtered2 that are not in filtered: 3 number of elemtns in filtered that are not in pseudo_filtered2: 2
# check 3, put a bound on std at 1700 (remove just 1 element)
# Filter columns based on mean and standard deviation
filtered_columns6 = pseudo_filtered2.columns[(pseudo_filtered2.std() < 1700)]
# Create pseudo_filtered dataset
pseudo_filtered6 = hcc_unfiltered[filtered_columns6]
pseudo_filtered6_summary=pseudo_filtered6.describe()
# similarity between filtered and pseudo_filtered6 data
# Convert the dataframes to sets
filtered_set = set(hcc_filtered.columns)
pseudo_set6 = set(pseudo_filtered6.columns)
unfiltered_set = set(hcc_unfiltered.columns)
# Calculate the intersection and union of the sets
intersection6 = filtered_set.intersection(pseudo_set6)
union6 = filtered_set.union(pseudo_set6)
# Calculate the Jaccard similarity coefficient
similarity6 = len(intersection6) / len(union6)
# Convert the similarity to percentage
similarity_percentage6 = similarity6 * 100
rem6=unfiltered_set - pseudo_set6
diff_6 = pseudo_set6 - filtered_set #elements in pseudo_filtered3 that are not in filtered
diff1_6 = filtered_set - pseudo_set6 #elements in filtered that are not in pseudo_filtered3
# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage6}%", "\nnumber of removed", len(rem6),
"\nnumber of elements in pseudo_filtered2 that are not in filtered:", len(diff_6),
"\nnumber of elemtns in filtered that are not in pseudo_filtered2:", len(diff1_6))
Similarity Percentage: 98.26086956521739% number of removed 14 number of elements in pseudo_filtered2 that are not in filtered: 3 number of elemtns in filtered that are not in pseudo_filtered2: 1
MEAN below 210 and no upper bound on STD is best filtering
FILTERING MANTAINING CELLS WITH MEAN OVER GENES BETWEEN 14 AND 210 AND STD OVER 82 WE REACH A SIMILIARITY PERCENTAGE WITH FILTERED DATAFRAME OF 98.26086956521739% :
TOTAL REMOVED 16
NUMBER OF ERRORS WHILE PSEUDO FILTERING: 3
NUMBER OF MISSED ELEMENTS WHILE PSEUDO FILTERING: 3
¶
2.2.4) TRIAL AND ERROR OF PSEUDO FILTERING ON GENES
We use same approach done before (see 2.1.4)
# similarity between filtered_transposed and unfiltred_transposed data
filtered_transposed_set=set(hcc_filtered_transposed.columns)
unfiltered_transposed_set=set(hcc_unfiltered_transposed.columns)
intersection=filtered_transposed_set.intersection(unfiltered_transposed_set)
union=filtered_transposed_set.union(unfiltered_transposed_set)
similarity_transposed=len(intersection)/len(union)
removed_transposed=unfiltered_transposed_set - filtered_transposed_set
print(f"Similarity Percentage: {similarity_transposed}%", "\nnumber of removed:", len(removed_transposed))
Similarity Percentage: 0.8336040348777569% number of removed: 3893
#trial 1: filter genes with mean below 0.8 and no filter on std
filtered_transposed_columns = hcc_unfiltered_transposed.columns[(hcc_unfiltered_transposed.mean() >= 0.05)]
pseudo_transposed_filtered = hcc_unfiltered_transposed[filtered_transposed_columns]
pseudo_transposed_filtered_summary=pseudo_transposed_filtered.describe()
pseudo_transposed_filtered.describe()
| "CICP27" | "DDX11L17" | "WASH9P" | "OR4F29" | "MTND1P23" | "MTND2P28" | "MTCO1P12" | "MTCO2P12" | "MTATP8P1" | "MTATP6P1" | ... | "MT-TH" | "MT-TS2" | "MT-TL2" | "MT-ND5" | "MT-ND6" | "MT-TE" | "MT-CYB" | "MT-TT" | "MT-TP" | "MAFIP" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | ... | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 | 243.000000 |
| mean | 0.119342 | 0.469136 | 0.255144 | 0.127572 | 117.930041 | 28.427984 | 904.308642 | 1.403292 | 0.378601 | 507.098765 | ... | 10.358025 | 5.930041 | 13.493827 | 2290.213992 | 386.901235 | 18.246914 | 2163.588477 | 20.613169 | 46.444444 | 3.897119 |
| std | 0.594531 | 1.455282 | 0.818639 | 0.440910 | 103.038022 | 26.062662 | 654.520308 | 1.735003 | 0.747361 | 394.423565 | ... | 10.910199 | 7.040559 | 11.803967 | 1726.423259 | 309.276105 | 54.076514 | 1730.393947 | 22.224590 | 47.684223 | 4.736193 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 46.500000 | 8.000000 | 390.500000 | 0.000000 | 0.000000 | 211.000000 | ... | 2.000000 | 1.000000 | 4.000000 | 918.000000 | 138.500000 | 4.000000 | 947.500000 | 5.000000 | 14.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 94.000000 | 22.000000 | 790.000000 | 1.000000 | 0.000000 | 428.000000 | ... | 7.000000 | 4.000000 | 10.000000 | 1848.000000 | 320.000000 | 11.000000 | 1774.000000 | 14.000000 | 38.000000 | 2.000000 |
| 75% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 157.000000 | 42.000000 | 1208.000000 | 2.000000 | 1.000000 | 684.000000 | ... | 14.000000 | 7.500000 | 20.000000 | 3172.000000 | 528.000000 | 20.000000 | 2927.000000 | 30.500000 | 64.500000 | 6.000000 |
| max | 5.000000 | 12.000000 | 6.000000 | 4.000000 | 694.000000 | 120.000000 | 3569.000000 | 12.000000 | 4.000000 | 2358.000000 | ... | 52.000000 | 43.000000 | 57.000000 | 8972.000000 | 1439.000000 | 804.000000 | 11383.000000 | 154.000000 | 409.000000 | 24.000000 |
8 rows × 20771 columns
pseudo_filtered_transposed_mean_max_value = pseudo_transposed_filtered_summary.iloc[1].max()
print("max mean value in pseudo filtered:", pseudo_filtered_transposed_mean_max_value)
hcc_filtered_transposed_mean_min_value = pseudo_transposed_filtered_summary.iloc[1].min()
print("min mean value in pseudo filtered:", hcc_filtered_transposed_mean_min_value)
max mean value in pseudo filtered: 27087.0 min mean value in pseudo filtered: 0.053497942386831275
sns.displot(pseudo_transposed_filtered_summary.iloc[1], bins=500)
plt.xlim(0, 25000)
plt.ylim(0, 1500)
plt.title("Distribution of means in pseudo pseudo_transposed_filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one
pseudo_filtered_transposed_std_max_value = pseudo_transposed_filtered_summary.iloc[2].max()
print("max std value in pseudo filtered:", pseudo_filtered_transposed_std_max_value)
hcc_filtered_transposed_mean_min_value = pseudo_transposed_filtered_summary.iloc[2].min()
print("min std value in pseudo filtered:", hcc_filtered_transposed_std_min_value)
max std value in pseudo filtered: 24669.076098596488 min std value in pseudo filtered: 0.1607698527031286
sns.displot(pseudo_transposed_filtered_summary.iloc[2], bins=500)
plt.xlim(0, 27200)
plt.ylim(0, 1500)
plt.title("Distribution of STD in pseudo pseudo_transposed_filtered")
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
# similarity between filtered_transposed and pseudo_transposed_filtered data
filtered_transposed_set=set(hcc_filtered_transposed.columns)
pseudo_transposed_filtered_set=set(pseudo_transposed_filtered.columns)
unfiltered_transposed_set=set(hcc_unfiltered_transposed.columns)
intersection=filtered_transposed_set.intersection(pseudo_transposed_filtered_set)
union=filtered_transposed_set.union(pseudo_transposed_filtered)
similarity_transposed=len(intersection)/len(union)
similarity_percentage_transposed=similarity_transposed*100
removed=unfiltered_transposed_set - pseudo_transposed_filtered_set
diff_transposed=pseudo_transposed_filtered_set-filtered_transposed_set
diff1_transposed=filtered_transposed_set-pseudo_transposed_filtered_set
print(f"Similarity Percentage: {similarity_percentage_transposed}%", "\nnumber of removed:", len(removed),
"\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed),
"\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed))
Similarity Percentage: 89.51578749235331% number of removed: 2625 number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 1748 number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 480
# check 1 higher thresholds (mean=0.08)
filtered_transposed_columns_check = hcc_unfiltered_transposed.columns[(hcc_unfiltered_transposed.mean() >= 0.08)]
pseudo_transposed_filtered_check = hcc_unfiltered_transposed[filtered_transposed_columns_check]
pseudo_transposed_filtered_check_summary=pseudo_transposed_filtered_check.describe()
# similarity between filtered_transposed and pseudo_transposed_filtered data
filtered_transposed_set=set(hcc_filtered_transposed.columns)
pseudo_transposed_filtered_check_set=set(pseudo_transposed_filtered_check.columns)
unfiltered_transposed_set=set(hcc_unfiltered_transposed.columns)
intersection_check=filtered_transposed_set.intersection(pseudo_transposed_filtered_check_set)
union_check=filtered_transposed_set.union(pseudo_transposed_filtered_check)
similarity_transposed_check=len(intersection_check)/len(union_check)
similarity_percentage_transposed_check=similarity_transposed_check*100
removed_check=unfiltered_transposed_set - pseudo_transposed_filtered_check_set
diff_transposed_check=pseudo_transposed_filtered_check_set-filtered_transposed_set
diff1_transposed_check=filtered_transposed_set-pseudo_transposed_filtered_check_set
print(f"Similarity Percentage: {similarity_percentage_transposed_check}%", "\nnumber of removed:", len(removed_check),
"\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed_check),
"\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed_check))
Similarity Percentage: 89.06991231012506% number of removed: 3442 number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 1366 number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 915
# check 2 lower thresholds (mean=0.05)
filtered_transposed_columns_check2 = hcc_unfiltered_transposed.columns[(hcc_unfiltered_transposed.mean() >= 0.03)]
pseudo_transposed_filtered_check2 = hcc_unfiltered_transposed[filtered_transposed_columns_check2]
pseudo_transposed_filtered_check2_summary2=pseudo_transposed_filtered_check2.describe()
# similarity between filtered_transposed and pseudo_transposed_filtered data
filtered_transposed_set=set(hcc_filtered_transposed.columns)
pseudo_transposed_filtered_check2_set=set(pseudo_transposed_filtered_check2.columns)
unfiltered_transposed_set=set(hcc_unfiltered_transposed.columns)
intersection_check2=filtered_transposed_set.intersection(pseudo_transposed_filtered_check2_set)
union_check2=filtered_transposed_set.union(pseudo_transposed_filtered_check2)
similarity_transposed_check2=len(intersection_check2)/len(union_check2)
similarity_percentage_transposed_check2=similarity_transposed_check2*100
removed_check2=unfiltered_transposed_set - pseudo_transposed_filtered_check2_set
diff_transposed_check2=pseudo_transposed_filtered_check2_set-filtered_transposed_set
diff1_transposed_check2=filtered_transposed_set-pseudo_transposed_filtered_check2_set
print(f"Similarity Percentage: {similarity_percentage_transposed_check2}%", "\nnumber of removed:", len(removed_check2),
"\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed_check2),
"\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed_check2))
Similarity Percentage: 89.11583402336922% number of removed: 1789 number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 2235 number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 131
# check 3 introduce a std lower bound= 0.5
filtered_transposed_columns_check3 = hcc_unfiltered_transposed.columns[(hcc_unfiltered_transposed.std() >= 0.5)]
pseudo_transposed_filtered_check3 = hcc_unfiltered_transposed[filtered_transposed_columns_check3]
pseudo_transposed_filtered_check3_summary3=pseudo_transposed_filtered_check3.describe()
# similarity between filtered_transposed and pseudo_transposed_filtered data
filtered_transposed_set=set(hcc_filtered_transposed.columns)
pseudo_transposed_filtered_check3_set=set(pseudo_transposed_filtered_check3.columns)
unfiltered_transposed_set=set(hcc_unfiltered_transposed.columns)
intersection_check3=filtered_transposed_set.intersection(pseudo_transposed_filtered_check3_set)
union_check3=filtered_transposed_set.union(pseudo_transposed_filtered_check3)
similarity_transposed_check3=len(intersection_check3)/len(union_check3)
similarity_percentage_transposed_check3=similarity_transposed_check3*100
removed_check3=unfiltered_transposed_set - pseudo_transposed_filtered_check3_set
diff_transposed_check3=pseudo_transposed_filtered_check3_set-filtered_transposed_set
diff1_transposed_check3=filtered_transposed_set-pseudo_transposed_filtered_check3_set
print(f"Similarity Percentage: {similarity_percentage_transposed_check3}%", "\nnumber of removed:", len(removed_check3),
"\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed_check3),
"\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed_check3))
Similarity Percentage: 84.94150678521292% number of removed: 3377 number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 1867 number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 1351
mean=0.05, std=0 is best lower bound
best option is to put no upper bound on mean and std since smiliarity will decrease. Infact:
# put upper bound mean=26900, std=27100 (just 1 element is removed)
filtered_transposed_column_check2 = pseudo_transposed_filtered.columns[(pseudo_transposed_filtered.mean() <= 26900) & (pseudo_transposed_filtered.std() <= 27100)]
pseudo_transposed_filtered_check2 = pseudo_transposed_filtered[filtered_transposed_column_check2]
pseudo_transposed_filtered_check2_summary=pseudo_transposed_filtered_check2.describe()
#similarity between filtered_transposed and pseudo_transposed_filtered data
filtered_transposed_set=set(hcc_filtered_transposed.columns)
pseudo_transposed_filtered_set_check2=set(pseudo_transposed_filtered_check2.columns)
unfiltered_transposed_set=set(hcc_unfiltered_transposed.columns)
intersection_check2=filtered_transposed_set.intersection(pseudo_transposed_filtered_set_check2)
union_check2=filtered_transposed_set.union(pseudo_transposed_filtered_check2)
similarity_transposed_check2=len(intersection_check2)/len(union_check2)
similarity_percentage_transposed_check2=similarity_transposed_check2*100
removed_check2=unfiltered_transposed_set - pseudo_transposed_filtered_set_check2
diff_transposed_check2=pseudo_transposed_filtered_set_check2-filtered_transposed_set
diff1_transposed_check2=filtered_transposed_set-pseudo_transposed_filtered_set_check2
print(f"Similarity Percentage: {similarity_percentage_transposed_check2}%", "\nnumber of removed:", len(removed_check2),
"\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed_check2),
"\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed_check2))
Similarity Percentage: 89.51108183144324% number of removed: 2626 number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 1748 number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 481
ON GENES ONLY FILTER WE CAN DO IS MEAN ABOVE 0.05 to reach Similarity Percentage: 89.51578749235331%
number of removed: 3442
number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 1366
number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 915
¶
2.2.4bis) Merging the two approaches
intersection based on cell (pseudo_filtered4 intersects pseudo_transposed_filtered transposed)
intersection_cell = pseudo_filtered.columns.intersection(pseudo_transposed_filtered.T.columns)
intersection_cell_set=set(intersection_cell)
filtered_set=set(hcc_filtered.columns)
unfiltered_set=set(hcc_unfiltered.columns)
int=intersection_cell_set.intersection(filtered_set)
un=intersection_cell_set.union(filtered_set)
similarity_cell = len(int)/len(un)
similarity_cell_percentage = similarity_cell*100
removed_cell = unfiltered_set - intersection_cell_set
diff_cell = filtered_set - intersection_cell_set
diff1_cell = intersection_cell_set - filtered_set
print(f"Similarity Percentage: {similarity_cell_percentage}%", "\nnumber of removed", len(removed_cell),
"\nnumber of elements in cell intersection that are not in filtered:", len(diff_cell),
"\nnumber of elemtns in filtered that are not in cell intersection:", len(diff1_cell))
Similarity Percentage: 98.26839826839827% number of removed 12 number of elements in cell intersection that are not in filtered: 0 number of elemtns in filtered that are not in cell intersection: 4
intersection based on genes (pseudo_filtered4 transposef intersects pseudo_transposed_filtered)
genes_pseudo4=pseudo_filtered2.T
intersection_genes= genes_pseudo4.columns.intersection(pseudo_transposed_filtered.columns)
intersection_genes_set=set(intersection_genes)
filtered_set_transposed=set(hcc_filtered_transposed.columns)
unfiltered_set_transposed=set(hcc_unfiltered_transposed.columns)
int_genes=intersection_genes_set.intersection(filtered_set_transposed)
un_genes=intersection_genes_set.union(filtered_set_transposed)
similarity_genes = len(int_genes)/len(un_genes)
similarity_genes_percentage = similarity_genes*100
removed_genes = unfiltered_set_transposed - intersection_genes_set
diff_genes = filtered_set_transposed - intersection_genes_set
diff1_genes = intersection_genes_set - filtered_set_transposed
print(f"Similarity Percentage: {similarity_genes_percentage}%", "\nnumber of removed", len(removed_genes),
"\nnumber of elements in genes intersection that are not in filtered:", len(diff_genes),
"\nnumber of elemtns in filtered that are not in genes intersection:", len(diff1_genes))
Similarity Percentage: 89.51578749235331% number of removed 2625 number of elements in genes intersection that are not in filtered: 480 number of elemtns in filtered that are not in genes intersection: 1748
CONCLUSION: best option is to filter firstly on cells and then intersect the result with the intersection on genes
¶
2.2.5) PCA
df_filtered = pd.read_csv("SmartSeq/HCC1806_SmartS_Filtered_Data.txt",delimiter="\ ",engine='python',index_col=0)
print(df_filtered.shape)
df_filtered.head()
(19503, 227)
| "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate1A9_Normoxia_S20_Aligned.sortedByCoord.out.bam" | ... | "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" | "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "CICP27" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "DDX11L17" | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "WASH9P" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| "OR4F29" | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "MTND1P23" | 250 | 424 | 63 | 27 | 81 | 305 | 82 | 46 | 177 | 114 | ... | 205 | 113 | 47 | 27 | 35 | 146 | 37 | 83 | 47 | 249 |
5 rows × 227 columns
df_original = pd.read_csv("SmartSeq/HCC1806_SmartS_Unfiltered_Data.txt",delimiter="\ ",engine='python',index_col=0)
from sklearn.decomposition import PCA
# PCA to reduce dimensionality
pca = PCA(n_components=2) # Reducing to 50 principal components
data_filtered_reduced = pca.fit_transform(df_filtered.T)
data_original_reduced = pca.fit_transform(df_original.T)
def extract_condition(name):
match = re.search(r'(Norm|Hypo)', name)
if match:
return match.group(1)
return 'Unknown'
conditions = df_filtered.T.index.to_series().apply(extract_condition)
conditions_original = df_original.T.index.to_series().apply(extract_condition)
plt.figure(figsize=(12, 6))
# Assign colors to conditions manually
color_map = {'Hypo': 'red', 'Norm': 'blue', 'Unknown': 'green'}
colors = conditions.map(color_map)
plt.scatter(data_filtered_reduced[:, 0], data_filtered_reduced[:, 1], c=colors)
plt.title('PCA of Cells Colored by Condition')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i],
label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')
plt.show()
In this case, despite the filtering, it is still difficult to clearly separate the data by condition. This could mean that in the HCC1806 cells the genes are less expressive of the state of the cell; however, it could also mean that the data is just not fit to be visualized in 2 dimensions.
¶
2.2.6) Clustering
¶
2.2.6.1) Single linkage
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_filtered_reduced, 'single')
# Increase the figure size
plt.figure(figsize=(25, 10)) # You can adjust the size as necessary.
# Plotting a truncated dendrogram
dendrogram(
Z,
truncate_mode='lastp', # show only the last p merged clusters
p=50, # show only the last 50 merged clusters
leaf_rotation=90.,
leaf_font_size=12.,
show_contracted=True, # to get a sense of the dataset size if truncated
)
plt.title('Hierarchical Clustering Dendrogram (Single-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_original_reduced, 'single')
# Increase the figure size
plt.figure(figsize=(25, 10)) # You can adjust the size as necessary.
# Plotting a truncated dendrogram
dendrogram(
Z,
truncate_mode='lastp', # show only the last p merged clusters
p=50, # show only the last 50 merged clusters
leaf_rotation=90.,
leaf_font_size=12.,
show_contracted=True, # to get a sense of the dataset size if truncated
)
plt.title('Hierarchical Clustering Dendrogram (Single-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
¶
2.2.6.2) Average Linkage
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_filtered_reduced, 'average')
# Increase the figure size
plt.figure(figsize=(25, 10)) # You can adjust the size as necessary.
# Plotting a truncated dendrogram
dendrogram(
Z,
truncate_mode='lastp', # show only the last p merged clusters
p=50, # show only the last 50 merged clusters
leaf_rotation=90.,
leaf_font_size=12.,
show_contracted=True, # to get a sense of the dataset size if truncated
)
plt.title('Hierarchical Clustering Dendrogram (Average-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_original_reduced, 'average')
# Increase the figure size
plt.figure(figsize=(25, 10)) # You can adjust the size as necessary.
# Plotting a truncated dendrogram
dendrogram(
Z,
truncate_mode='lastp', # show only the last p merged clusters
p=50, # show only the last 50 merged clusters
leaf_rotation=90.,
leaf_font_size=12.,
show_contracted=True, # to get a sense of the dataset size if truncated
)
plt.title('Hierarchical Clustering Dendrogram (-Average-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
¶
2.2.6.3) Complete Linkage
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_filtered_reduced, 'complete')
# Increase the figure size
plt.figure(figsize=(25, 10)) # You can adjust the size as necessary.
# Plotting a truncated dendrogram
dendrogram(
Z,
truncate_mode='lastp', # show only the last p merged clusters
p=50, # show only the last 50 merged clusters
leaf_rotation=90.,
leaf_font_size=12.,
show_contracted=True, # to get a sense of the dataset size if truncated
)
plt.title('Hierarchical Clustering Dendrogram (Complete-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_original_reduced, 'complete')
# Increase the figure size
plt.figure(figsize=(25, 10)) # You can adjust the size as necessary.
# Plotting a truncated dendrogram
dendrogram(
Z,
truncate_mode='lastp', # show only the last p merged clusters
p=50, # show only the last 50 merged clusters
leaf_rotation=90.,
leaf_font_size=12.,
show_contracted=True, # to get a sense of the dataset size if truncated
)
plt.title('Hierarchical Clustering Dendrogram (Complete-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
¶
2.2.7) Unsupervised learning applied to HCC1806
# Function to extract 'Normoxia' or 'Hypoxia' from a cell name
def extract_condition(name):
match = re.search(r'(Normoxia|Hypoxia)', name)
if match:
return match.group(1)
return 'Unknown'
df_HCC1806_norm_filt = pd.read_csv("SmartSeq/HCC1806_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)
df_HCC1806_norm_filt_transpose = df_HCC1806_norm_filt.T
# We transpose the original dataset so we make the cells to be the rows since they represents the observations
# and the genes to be columns since they are the features
# Apply the extract_condition function to each cell name in the DataFrame's index
conditions = df_HCC1806_norm_filt_transpose.index.to_series().apply(extract_condition)
# We apply the pca to our dataset
# We basically reduce the dimensionality of our dataset to 2 dimensions
# because we want to see the data in a 2D space
pca = PCA(n_components=2)
principal_components = pca.fit_transform(df_HCC1806_norm_filt_transpose)
# now based on what we have reduced we apply the k-means to try to clusters cells in two groups
# The groups should be based on the cell with Normoxia and Hypoxia
# Please notice that this k-means should use the so called k++ to initialize the centroids
# If you don't know what k++ is, please add init keywords and set it to something different
k = 2
kmeans = KMeans(n_clusters=k, init='k-means++', random_state=42)
clusters = kmeans.fit_predict(principal_components)
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1. warnings.warn(
# Assign numeric labels based on conditions
condition_numeric = conditions.map({'Normoxia': 0, 'Hypoxia': 1})
plt.figure(figsize=(12, 6))
# Assign colors to conditions manually
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue', 'Unknown': 'green'}
colors = conditions.map(color_map)
plt.scatter(principal_components[:, 0], principal_components[:, 1], c=colors)
plt.title('PCA of Cells Colored by Condition')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i],
label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')
plt.show()
# Visualization of PCA with K-means clusters, with a proper legend
plt.figure(figsize=(12, 6))
# Assign colors based on the K-means cluster result
# Here we assign 'yellow' to cluster 1 and 'blue' to cluster 0
cluster_colors = ['green' if label == 0 else 'yellow' for label in clusters]
# Scatter plot with assigned colors
scatter = plt.scatter(principal_components[:, 0], principal_components[:, 1], c=cluster_colors)
# Create a legend for the colors
plt.title('PCA of Cells Colored by K-means Clusters')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
# Assuming that after any potential inversion, cluster 0 corresponds to Normoxia and cluster 1 to Hypoxia
legend_labels = {0: 'Cluster1', 1: 'Cluster2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Condition')
plt.show()
Unlike before with the MCF7 cells, the PCA performed on the HCC1806 cells does not give a clear cut division of the conditions of the cells. In order to have a more useful result, we will try again to see which are the genes that improve the most our ability to read the condition of the cells
¶
2.2.8) Looking for the most useful genes
#i want to split the dataset in 2 parts, one with RNA with hypoxia in the name and the other with RNA with normoxia in the name
hypoxia_samples = df_HCC1806_norm_filt.T[df_HCC1806_norm_filt.T.index.str.contains('Hypo')]
normoxia_samples = df_HCC1806_norm_filt.T[df_HCC1806_norm_filt.T.index.str.contains('Norm')]
hypoxia_samples
| "DDIT4" | "ANGPTL4" | "CALML5" | "KRT14" | "CCNB1" | "IGFBP3" | "AKR1C2" | "KRT6A" | "NDRG1" | "KRT4" | ... | "MST1R" | "ZYG11A" | "NRG1" | "RBMS3" | "VCPIP1" | "LINC02693" | "OR8B9P" | "NEAT1" | "ZDHHC23" | "ODAD2" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "output.STAR.PCRPlate1G1_Hypoxia_S102_Aligned.sortedByCoord.out.bam" | 8739 | 2101 | 55 | 96 | 1824 | 1938 | 62 | 0 | 522 | 413 | ... | 279 | 0 | 264 | 0 | 134 | 68 | 0 | 213 | 0 | 0 |
| "output.STAR.PCRPlate1G2_Hypoxia_S2_Aligned.sortedByCoord.out.bam" | 13098 | 14032 | 0 | 0 | 1616 | 247 | 430 | 907 | 348 | 0 | ... | 311 | 0 | 38 | 0 | 0 | 0 | 0 | 92 | 0 | 0 |
| "output.STAR.PCRPlate1G3_Hypoxia_S7_Aligned.sortedByCoord.out.bam" | 2880 | 356 | 0 | 6211 | 3 | 3430 | 79 | 1953 | 592 | 176 | ... | 125 | 0 | 16 | 0 | 4 | 1 | 0 | 1 | 0 | 0 |
| "output.STAR.PCRPlate1G4_Hypoxia_S107_Aligned.sortedByCoord.out.bam" | 7777 | 5661 | 4383 | 0 | 145 | 4618 | 246 | 85 | 206 | 0 | ... | 268 | 1 | 25 | 0 | 0 | 0 | 0 | 128 | 0 | 0 |
| "output.STAR.PCRPlate1H1_Hypoxia_S103_Aligned.sortedByCoord.out.bam" | 10313 | 850 | 0 | 2127 | 605 | 0 | 381 | 1985 | 138 | 0 | ... | 49 | 20 | 239 | 0 | 10 | 79 | 0 | 157 | 35 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| "output.STAR.PCRPlate4F5_Hypoxia_S229_Aligned.sortedByCoord.out.bam" | 3717 | 2500 | 0 | 111 | 1447 | 2068 | 356 | 5 | 301 | 0 | ... | 153 | 0 | 280 | 26 | 111 | 0 | 0 | 13 | 0 | 0 |
| "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" | 5545 | 2085 | 1587 | 0 | 26 | 1085 | 17 | 806 | 585 | 0 | ... | 180 | 0 | 158 | 18 | 26 | 68 | 0 | 163 | 0 | 0 |
| "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" | 5757 | 698 | 0 | 3077 | 50 | 1408 | 619 | 2968 | 1775 | 0 | ... | 82 | 0 | 276 | 0 | 2 | 116 | 0 | 5 | 0 | 0 |
| "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" | 8337 | 2383 | 0 | 529 | 588 | 456 | 316 | 277 | 1461 | 58 | ... | 156 | 0 | 35 | 0 | 47 | 27 | 0 | 19 | 38 | 0 |
| "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" | 10186 | 3147 | 0 | 149 | 1012 | 111 | 716 | 617 | 744 | 0 | ... | 94 | 0 | 55 | 0 | 157 | 43 | 0 | 113 | 0 | 0 |
97 rows × 3000 columns
hypoxia_summary = hypoxia_samples.describe()
normoxia_summary = normoxia_samples.describe()
normoxia_summary
| "DDIT4" | "ANGPTL4" | "CALML5" | "KRT14" | "CCNB1" | "IGFBP3" | "AKR1C2" | "KRT6A" | "NDRG1" | "KRT4" | ... | "MST1R" | "ZYG11A" | "NRG1" | "RBMS3" | "VCPIP1" | "LINC02693" | "OR8B9P" | "NEAT1" | "ZDHHC23" | "ODAD2" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 85.000000 | 85.000000 | 85.000000 | 85.000000 | 85.000000 | 85.000000 | 85.000000 | 85.000000 | 85.000000 | 85.000000 | ... | 85.000000 | 85.000000 | 85.000000 | 85.000000 | 85.000000 | 85.000000 | 85.000000 | 85.000000 | 85.000000 | 85.000000 |
| mean | 529.011765 | 46.717647 | 307.352941 | 871.094118 | 1211.870588 | 376.564706 | 1941.188235 | 1590.517647 | 32.400000 | 213.952941 | ... | 106.329412 | 5.129412 | 151.611765 | 5.435294 | 37.905882 | 67.388235 | 0.247059 | 63.223529 | 14.670588 | 2.117647 |
| std | 864.408682 | 170.988903 | 643.410494 | 1316.641015 | 1535.550582 | 723.431365 | 3121.412815 | 1881.465567 | 163.216858 | 811.675016 | ... | 118.997338 | 11.811806 | 141.017146 | 11.800370 | 48.971231 | 85.042300 | 1.479268 | 65.789595 | 34.277019 | 7.577240 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | 0.000000 | 0.000000 |
| 25% | 79.000000 | 0.000000 | 0.000000 | 21.000000 | 177.000000 | 41.000000 | 291.000000 | 544.000000 | 0.000000 | 0.000000 | ... | 2.000000 | 0.000000 | 42.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 21.000000 | 0.000000 | 0.000000 |
| 50% | 247.000000 | 2.000000 | 0.000000 | 327.000000 | 489.000000 | 137.000000 | 893.000000 | 884.000000 | 1.000000 | 0.000000 | ... | 87.000000 | 0.000000 | 120.000000 | 0.000000 | 21.000000 | 38.000000 | 0.000000 | 42.000000 | 0.000000 | 0.000000 |
| 75% | 679.000000 | 41.000000 | 163.000000 | 1151.000000 | 2207.000000 | 297.000000 | 2200.000000 | 1980.000000 | 26.000000 | 0.000000 | ... | 144.000000 | 1.000000 | 229.000000 | 4.000000 | 57.000000 | 109.000000 | 0.000000 | 82.000000 | 16.000000 | 0.000000 |
| max | 6278.000000 | 1532.000000 | 2867.000000 | 6724.000000 | 6914.000000 | 4454.000000 | 20195.000000 | 9584.000000 | 1507.000000 | 4560.000000 | ... | 751.000000 | 52.000000 | 763.000000 | 49.000000 | 239.000000 | 498.000000 | 11.000000 | 415.000000 | 222.000000 | 48.000000 |
8 rows × 3000 columns
# I want to do the mean of the mean in the hypoxia samples and return the value
hypoxia_mean = hypoxia_samples.describe().loc['mean'].mean()
hypoxia_mean
180.94042268041235
# Now let's dothe same with the standard deviation
hypoxia_std = hypoxia_samples.describe().loc['std'].mean()
hypoxia_std
148.89673483016463
normoxia_samples
| "DDIT4" | "ANGPTL4" | "CALML5" | "KRT14" | "CCNB1" | "IGFBP3" | "AKR1C2" | "KRT6A" | "NDRG1" | "KRT4" | ... | "MST1R" | "ZYG11A" | "NRG1" | "RBMS3" | "VCPIP1" | "LINC02693" | "OR8B9P" | "NEAT1" | "ZDHHC23" | "ODAD2" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "output.STAR.PCRPlate1G12_Normoxia_S32_Aligned.sortedByCoord.out.bam" | 0 | 48 | 0 | 321 | 298 | 82 | 6250 | 634 | 0 | 0 | ... | 78 | 10 | 136 | 0 | 0 | 29 | 0 | 29 | 0 | 0 |
| "output.STAR.PCRPlate1G7_Normoxia_S118_Aligned.sortedByCoord.out.bam" | 208 | 0 | 1902 | 0 | 378 | 451 | 7155 | 2932 | 41 | 0 | ... | 169 | 0 | 339 | 0 | 0 | 1 | 0 | 56 | 0 | 0 |
| "output.STAR.PCRPlate1G8_Normoxia_S19_Aligned.sortedByCoord.out.bam" | 750 | 0 | 0 | 5050 | 66 | 44 | 7533 | 793 | 42 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 415 | 0 | 0 |
| "output.STAR.PCRPlate1G9_Normoxia_S121_Aligned.sortedByCoord.out.bam" | 127 | 0 | 0 | 462 | 58 | 297 | 2216 | 26 | 15 | 0 | ... | 107 | 0 | 193 | 0 | 23 | 195 | 0 | 42 | 18 | 0 |
| "output.STAR.PCRPlate1H9_Normoxia_S122_Aligned.sortedByCoord.out.bam" | 404 | 32 | 0 | 1893 | 2796 | 27 | 1496 | 626 | 81 | 0 | ... | 0 | 6 | 201 | 0 | 239 | 160 | 0 | 30 | 23 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" | 2579 | 65 | 0 | 156 | 411 | 147 | 107 | 1213 | 1 | 0 | ... | 1 | 0 | 221 | 0 | 0 | 1 | 0 | 4 | 145 | 0 |
| "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" | 79 | 5 | 0 | 2098 | 554 | 99 | 798 | 4211 | 64 | 0 | ... | 11 | 0 | 264 | 0 | 10 | 34 | 0 | 24 | 0 | 0 |
| "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" | 167 | 1 | 0 | 27 | 3585 | 134 | 3 | 596 | 11 | 0 | ... | 251 | 0 | 120 | 25 | 0 | 53 | 0 | 177 | 53 | 0 |
| "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" | 238 | 41 | 2867 | 814 | 2207 | 297 | 1023 | 2879 | 1 | 0 | ... | 149 | 0 | 42 | 0 | 26 | 0 | 0 | 13 | 5 | 0 |
| "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" | 60 | 0 | 0 | 7 | 477 | 10 | 845 | 1222 | 1 | 0 | ... | 98 | 7 | 255 | 0 | 32 | 25 | 0 | 13 | 4 | 0 |
85 rows × 3000 columns
normoxia_mean = normoxia_samples.describe().loc['mean'].mean()
normoxia_mean
152.2196431372549
normoxia_std = normoxia_samples.describe().loc['std'].mean()
normoxia_std
113.76597693898488
#we look at how many genes have a mean of expression greater than 1 and we print their names
#for hypoxia samples
hypoxia_samples_summary = hypoxia_samples.describe()
hypo_genes = hypoxia_samples_summary.loc['mean'][hypoxia_samples_summary.loc['mean'] > 180.94]
hypo_genes_sorted = sorted(hypo_genes.items(), key=lambda x: x[1], reverse=True)
hypo_genes_sorted # 384 genes overcome the mean threshold
[('"LDHA"', 24055.381443298967),
('"FTL"', 21462.113402061856),
('"FTH1"', 13867.432989690722),
('"KRT19"', 13597.38144329897),
('"BEST1"', 12069.711340206186),
('"ALDOA"', 10299.670103092783),
('"HSPA5"', 10243.113402061856),
('"CD44"', 9826.247422680412),
('"MT-RNR2"', 8343.61855670103),
('"DDIT4"', 7114.268041237114),
('"LAMB3"', 6433.041237113402),
('"HSP90B1"', 6299.298969072165),
('"LAMC2"', 5953.453608247422),
('"CD59"', 5605.412371134021),
('"PSMD2"', 5376.79381443299),
('"MT-ND4"', 5357.030927835051),
('"B2M"', 5326.484536082474),
('"PGK1"', 5289.773195876289),
('"MT-CO2"', 4634.030927835051),
('"TMSB10"', 4603.701030927835),
('"KRT18"', 4585.144329896907),
('"UBC"', 4376.5360824742265),
('"KRT8"', 4122.443298969072),
('"EZR"', 3632.783505154639),
('"MIF"', 3630.278350515464),
('"MIF-AS1"', 3620.092783505155),
('"ACTG1"', 3431.494845360825),
('"TMSB4X"', 3368.103092783505),
('"ANXA1"', 3352.360824742268),
('"CALM2"', 3079.701030927835),
('"CAV1"', 3077.175257731959),
('"HSPA8"', 3043.8865979381444),
('"SDHA"', 2932.381443298969),
('"KRT7"', 2919.659793814433),
('"LMNA"', 2778.4123711340208),
('"P4HB"', 2762.577319587629),
('"PRDX1"', 2740.896907216495),
('"SCD"', 2739.577319587629),
('"TUBB"', 2673.938144329897),
('"GPI"', 2622.773195876289),
('"PSAP"', 2604.938144329897),
('"MT-ND5"', 2507.061855670103),
('"SLC2A1"', 2456.9690721649486),
('"TUBA1B"', 2449.463917525773),
('"ANGPTL4"', 2261.577319587629),
('"CTSB"', 2224.4845360824743),
('"CANX"', 2199.422680412371),
('"TK1"', 2191.7525773195875),
('"S100A2"', 2165.2474226804125),
('"F3"', 2160.2474226804125),
('"IGFBP3"', 2055.3092783505153),
('"MT-CYB"', 2020.8969072164948),
('"HMGA1"', 1983.9072164948454),
('"GPRC5A"', 1980.1855670103093),
('"UBB"', 1974.5463917525774),
('"MT-CO3"', 1969.9484536082475),
('"FDFT1"', 1949.8041237113403),
('"SLC3A2"', 1931.4742268041236),
('"MT-ND4L"', 1908.9690721649486),
('"KRT6A"', 1851.3711340206185),
('"LDHB"', 1846.7113402061855),
('"TXN"', 1813.7731958762886),
('"PLIN2"', 1804.4845360824743),
('"GPX3"', 1768.5051546391753),
('"DHCR24"', 1761.3917525773195),
('"MALAT1"', 1684.2268041237114),
('"UCA1"', 1673.7319587628865),
('"TACSTD2"', 1649.1237113402062),
('"FAM83A"', 1618.639175257732),
('"DHCR7"', 1582.1649484536083),
('"H2AZ1"', 1576.5979381443299),
('"STMN1"', 1556.4948453608247),
('"ITGA6"', 1545.701030927835),
('"PHLDA1"', 1526.4020618556701),
('"ADIRF-AS1"', 1519.4432989690722),
('"CSTB"', 1507.041237113402),
('"ADIRF"', 1475.7525773195875),
('"BLCAP"', 1404.8041237113403),
('"KRT17"', 1387.6494845360826),
('"HSP90AA1"', 1384.5051546391753),
('"TINAGL1"', 1374.319587628866),
('"ACTN1"', 1358.8453608247423),
('"KRT13"', 1356.721649484536),
('"JUP"', 1350.1443298969073),
('"S100A16"', 1340.4329896907216),
('"FOS"', 1338.8041237113403),
('"GAS6"', 1286.9278350515465),
('"TMBIM1"', 1281.1958762886597),
('"FOSL1"', 1265.6907216494844),
('"SFN"', 1225.3298969072166),
('"TUBB4B"', 1219.0515463917525),
('"UPK1B"', 1190.4742268041236),
('"ERO1A"', 1181.680412371134),
('"PXN"', 1180.5257731958764),
('"SQLE"', 1178.2474226804125),
('"S100A6"', 1133.5567010309278),
('"SQSTM1"', 1132.6907216494844),
('"SLC1A5"', 1121.6082474226805),
('"MT2A"', 1117.319587628866),
('"PERP"', 1113.701030927835),
('"BNIP3"', 1110.979381443299),
('"KRT5"', 1106.3917525773195),
('"MYH9"', 1104.0927835051546),
('"SERPINB5"', 1102.7835051546392),
('"LAMA3"', 1097.4742268041236),
('"FSCN1"', 1090.2061855670104),
('"GLUL"', 1079.9484536082475),
('"TFRC"', 1076.2474226804125),
('"COL17A1"', 1024.2886597938145),
('"NQO1"', 1021.4226804123712),
('"SEC61A1"', 1017.1443298969073),
('"ACAT2"', 1016.4742268041238),
('"CTSD"', 1010.4123711340206),
('"GRN"', 1010.0927835051547),
('"MAL2"', 1003.4536082474227),
('"IDI1"', 989.5876288659794),
('"JUNB"', 988.7731958762887),
('"KPNA2"', 986.7010309278351),
('"SLC38A2"', 980.680412371134),
('"FLNB"', 976.5360824742268),
('"KRT14"', 965.3092783505155),
('"CDC20"', 960.4536082474227),
('"RRM2"', 958.319587628866),
('"ST14"', 943.2268041237113),
('"AKR1C2"', 940.5360824742268),
('"FLNA"', 937.2577319587629),
('"HLA-A"', 927.3092783505155),
('"FXYD3"', 907.8247422680413),
('"KDM1A"', 900.7835051546392),
('"SLC25A1"', 898.9175257731958),
('"TUBB6"', 898.8659793814433),
('"ITGB4"', 887.1649484536083),
('"FGFBP1"', 883.6082474226804),
('"CAVIN3"', 873.2164948453608),
('"G0S2"', 873.1649484536083),
('"PRNP"', 871.6288659793814),
('"PLAU"', 864.9381443298969),
('"H4C3"', 852.4020618556701),
('"NDRG1"', 830.7525773195877),
('"P4HA1"', 824.0412371134021),
('"NFKBIA"', 819.340206185567),
('"IRF6"', 818.1134020618557),
('"MCM3"', 809.659793814433),
('"PLK2"', 804.4536082474227),
('"PTHLH"', 794.0103092783505),
('"TIMP3"', 793.4845360824743),
('"LAMC1"', 784.1237113402062),
('"PHLDA1-AS1"', 782.4536082474227),
('"AMIGO2"', 780.8556701030927),
('"ZFP36L1"', 779.0618556701031),
('"SLCO4A1"', 777.6907216494845),
('"TPX2"', 776.0309278350516),
('"CDKN1A"', 774.1134020618557),
('"BHLHE40"', 764.3298969072165),
('"CDK2AP2"', 754.2886597938144),
('"SLC7A5"', 739.8865979381443),
('"HK1"', 735.7422680412371),
('"SEMA4B"', 731.659793814433),
('"TXNRD1"', 724.0927835051547),
('"PYGB"', 719.9175257731958),
('"TPBG"', 712.7422680412371),
('"PCDH1"', 704.7731958762887),
('"MT-RNR1"', 696.0515463917526),
('"PFKFB3"', 687.9072164948453),
('"AHNAK"', 687.659793814433),
('"CLDN4"', 673.7835051546392),
('"LIPA"', 670.9587628865979),
('"DSP"', 668.1649484536083),
('"ALDOC"', 666.319587628866),
('"COPG1"', 656.4536082474227),
('"WARS1"', 655.7113402061856),
('"NET1"', 651.0309278350516),
('"HSPH1"', 649.2474226804123),
('"AKR1B1"', 645.2268041237113),
('"THBS1"', 638.8865979381443),
('"HERPUD1"', 636.6082474226804),
('"INSIG1"', 633.9587628865979),
('"IER2"', 628.1649484536083),
('"ARL6IP1"', 624.7835051546392),
('"SLC6A8"', 623.8350515463917),
('"DUSP1"', 613.5979381443299),
('"CPA4"', 613.1958762886597),
('"LDLR"', 607.1134020618557),
('"BIRC5"', 602.1958762886597),
('"C19orf48"', 599.5154639175257),
('"PFKL"', 598.5876288659794),
('"POLR2A"', 591.9690721649484),
('"MCM7"', 591.3814432989691),
('"HMGB2"', 591.3711340206186),
('"PPP1R15A"', 586.4742268041238),
('"EHF"', 584.1134020618557),
('"DUSP6"', 582.7938144329897),
('"CDH1"', 581.2164948453608),
('"CALB1"', 570.8556701030927),
('"FJX1"', 568.9484536082474),
('"GPX2"', 568.7422680412371),
('"CCNB1"', 564.9587628865979),
('"AKR1C3"', 564.9175257731958),
('"MSMO1"', 562.4226804123712),
('"FAM162A"', 560.7525773195877),
('"TRIB3"', 559.0721649484536),
('"KRT4"', 556.7216494845361),
('"MCM4"', 552.9381443298969),
('"TRIM29"', 544.9484536082474),
('"MEST"', 539.979381443299),
('"DKK1"', 538.8556701030927),
('"POP4"', 535.1958762886597),
('"SPP1"', 532.8659793814433),
('"UGT1A10"', 531.5463917525773),
('"NUSAP1"', 525.1134020618557),
('"PRSS23"', 520.1237113402062),
('"UGT1A6"', 516.3298969072165),
('"AURKB"', 510.41237113402065),
('"MET"', 510.1855670103093),
('"ATAD2"', 508.77319587628864),
('"ARNTL2"', 506.49484536082474),
('"CAVIN1"', 505.5979381443299),
('"MRNIP"', 501.5670103092784),
('"EIF5"', 500.08247422680415),
('"GLTP"', 499.4020618556701),
('"CLDN7"', 497.03092783505156),
('"AARS1"', 495.8556701030928),
('"PHGDH"', 493.92783505154637),
('"HMGCR"', 488.70103092783506),
('"PTP4A1"', 486.44329896907215),
('"ARRDC3"', 485.1340206185567),
('"SRXN1"', 479.7525773195876),
('"CALML5"', 477.7628865979381),
('"ID1"', 477.1546391752577),
('"MCM2"', 475.25773195876286),
('"PSAT1"', 468.659793814433),
('"SERPINE1"', 464.12371134020617),
('"BRD2"', 463.36082474226805),
('"GFPT1"', 459.07216494845363),
('"SDC1"', 456.30927835051546),
('"EIF4A2"', 448.11340206185565),
('"SRM"', 445.12371134020617),
('"ITGA5"', 443.07216494845363),
('"MYBL2"', 441.1855670103093),
('"IRAK1"', 440.1855670103093),
('"SLC20A1"', 439.0515463917526),
('"TSC22D1"', 437.8144329896907),
('"NUCB2"', 436.70103092783506),
('"HSPB1"', 433.2474226804124),
('"UBE2C"', 432.87628865979383),
('"C4orf3"', 432.6701030927835),
('"CDC6"', 432.50515463917526),
('"BNIP3L"', 427.74226804123714),
('"PPARG"', 427.5979381443299),
('"ZWINT"', 423.50515463917526),
('"UAP1"', 421.9072164948454),
('"ANLN"', 420.6701030927835),
('"NCAPD2"', 420.54639175257734),
('"KLK10"', 415.0),
('"MKI67"', 413.02061855670104),
('"KLF6"', 411.9587628865979),
('"MCM5"', 410.36082474226805),
('"FEN1"', 408.74226804123714),
('"RNF26"', 408.340206185567),
('"EFEMP1"', 407.9896907216495),
('"GYS1"', 406.0618556701031),
('"MSH6"', 405.2680412371134),
('"KDM5B"', 405.07216494845363),
('"HES1"', 403.680412371134),
('"EGLN3"', 398.70103092783506),
('"CYP51A1"', 395.5670103092784),
('"TRIM16"', 395.340206185567),
('"ERRFI1"', 393.9484536082474),
('"CTNNAL1"', 393.6082474226804),
('"RBM14"', 392.8144329896907),
('"NOLC1"', 391.07216494845363),
('"FN1"', 385.1546391752577),
('"STC2"', 384.5670103092784),
('"P4HA2"', 384.2371134020619),
('"ADM"', 383.3298969072165),
('"PRC1"', 374.2886597938144),
('"UNG"', 374.2783505154639),
('"PCNA"', 374.1958762886598),
('"BCAR1"', 374.0618556701031),
('"PTTG1"', 373.340206185567),
('"CDK1"', 373.2371134020619),
('"PLOD2"', 371.5360824742268),
('"TOP2A"', 366.69072164948454),
('"LRRC8A"', 363.4845360824742),
('"ID3"', 362.680412371134),
('"DCBLD2"', 361.5257731958763),
('"VEGFA"', 358.0103092783505),
('"CCNB2"', 354.0927835051546),
('"NUP188"', 350.74226804123714),
('"ABLIM1"', 350.3917525773196),
('"CA9"', 349.5257731958763),
('"FADS1"', 349.37113402061857),
('"KRT16"', 348.0618556701031),
('"FOSL2"', 345.91752577319585),
('"SSR3"', 343.17525773195877),
('"BAG3"', 342.6701030927835),
('"EGLN1"', 342.3814432989691),
('"UGDH"', 341.659793814433),
('"MMP1"', 341.55670103092785),
('"COL4A2"', 337.77319587628864),
('"PINK1"', 337.5257731958763),
('"CEACAM5"', 335.70103092783506),
('"EMP1"', 334.08247422680415),
('"CRKL"', 330.340206185567),
('"UBE2S"', 329.7216494845361),
('"ISG15"', 327.4329896907216),
('"EPHA2"', 326.77319587628864),
('"LSS"', 326.1443298969072),
('"LINC01764"', 325.58762886597935),
('"AVPI1"', 324.91752577319585),
('"KIF20A"', 321.9896907216495),
('"AMOTL2"', 318.4020618556701),
('"KIF22"', 316.77319587628864),
('"TOB1"', 315.37113402061857),
('"TGFBI"', 312.78350515463916),
('"LOXL2"', 312.3814432989691),
('"POLD1"', 311.11340206185565),
('"SAT1"', 310.7938144329897),
('"PXDN"', 308.9896907216495),
('"ANXA3"', 305.74226804123714),
('"SERPINE2"', 304.1855670103093),
('"H1-0"', 302.1855670103093),
('"PCSK9"', 301.2474226804124),
('"DLK2"', 299.08247422680415),
('"HILPDA"', 298.62886597938143),
('"DTL"', 298.0927835051546),
('"VARS1"', 297.2474226804124),
('"GAS6-AS1"', 294.36082474226805),
('"DUSP10"', 293.2886597938144),
('"ITGB5"', 292.96907216494844),
('"KIAA0040"', 292.29896907216494),
('"TNFRSF21"', 286.70103092783506),
('"KCTD11"', 286.16494845360825),
('"C1orf116"', 282.87628865979383),
('"UBE2T"', 280.4742268041237),
('"RND3"', 278.41237113402065),
('"DHRS3"', 277.5773195876289),
('"VSIR"', 273.7525773195876),
('"MVD"', 273.6701030927835),
('"KLF10"', 272.3298969072165),
('"SREBF2"', 271.9381443298969),
('"BCAR3"', 270.55670103092785),
('"SNAI2"', 268.96907216494844),
('"TNFAIP2"', 268.55670103092785),
('"TM4SF1"', 268.0515463917526),
('"LVRN"', 264.91752577319585),
('"PDLIM2"', 262.44329896907215),
('"MYC"', 261.2783505154639),
('"FABP5"', 260.10309278350513),
('"IL20RB"', 259.8041237113402),
('"PPIF"', 258.17525773195877),
('"TCF19"', 254.27835051546393),
('"ALDH3A1"', 253.41237113402062),
('"OAS1"', 246.1237113402062),
('"ACSS2"', 243.7422680412371),
('"SPAG5"', 242.83505154639175),
('"MT1X"', 240.9278350515464),
('"AJUBA"', 240.7319587628866),
('"SMG7"', 239.43298969072166),
('"AXL"', 238.57731958762886),
('"TGFBR2"', 238.51546391752578),
('"PPRC1"', 236.88659793814432),
('"ALDH1A3"', 236.7835051546392),
('"SMURF2"', 236.4536082474227),
('"HMGCS1"', 236.43298969072166),
('"NEU1"', 234.04123711340208),
('"MCM6"', 233.63917525773195),
('"ITGA2"', 232.23711340206185),
('"TXNIP"', 231.1340206185567),
('"MCM10"', 228.7835051546392),
('"TMEM200A"', 227.51546391752578),
('"PTGES"', 227.4536082474227),
('"PLEKHF1"', 224.36082474226805),
('"RIPK4"', 223.340206185567),
('"UHRF1"', 222.23711340206185),
('"CDH13"', 219.64948453608247),
('"MXD1"', 217.09278350515464),
('"EGR1"', 216.5257731958763),
('"GPNMB"', 216.30927835051546),
('"LY6D"', 213.51546391752578),
('"KNSTRN"', 213.35051546391753),
('"MOB3A"', 212.4020618556701),
('"FUT11"', 212.16494845360825),
('"FST"', 209.1340206185567),
('"SEC24D"', 206.70103092783506),
('"KIF23"', 205.20618556701032),
('"RBCK1"', 203.94845360824743),
('"NID2"', 202.28865979381445),
('"KIF2C"', 201.8041237113402),
('"CKS2"', 201.36082474226805),
('"TNFAIP3"', 201.0),
('"XDH"', 200.84536082474227),
('"GINS2"', 200.02061855670104),
('"KLK5"', 199.659793814433),
('"SLC37A2"', 199.09278350515464),
('"SYTL3"', 198.44329896907217),
('"AHNAK2"', 198.17525773195877),
('"FSTL1"', 197.79381443298968),
('"HAS2"', 197.56701030927834),
('"CLDN1"', 197.53608247422682),
('"ASF1B"', 196.02061855670104),
('"LGALS1"', 195.27835051546393),
('"CD74"', 195.02061855670104),
('"KIF11"', 194.55670103092783),
('"MELK"', 194.53608247422682),
('"CCNE1"', 194.43298969072166),
('"HK2"', 193.39175257731958),
('"CCNA2"', 192.46391752577318),
('"CDC45"', 191.01030927835052),
('"LIG1"', 190.5979381443299),
('"YPEL5"', 189.42268041237114),
('"CRIP2"', 189.11340206185568),
('"FAM111A"', 187.01030927835052),
('"PPL"', 186.5463917525773),
('"KCNMA1"', 186.17525773195877),
('"ANO1"', 186.08247422680412),
('"NECTIN2"', 185.7422680412371),
('"RFWD3"', 185.3298969072165),
('"WNT10A"', 185.1443298969072),
('"CEP55"', 184.77319587628867),
('"SLPI"', 182.79381443298968),
('"AKR1C1"', 181.49484536082474),
('"TMEM45A"', 181.28865979381445),
('"SLC43A3"', 181.16494845360825),
('"NRP1"', 181.06185567010309)]
normoxia_samples_summary = normoxia_samples.describe()
normo_genes = normoxia_samples_summary.loc['mean'][normoxia_samples_summary.loc['mean'] > 152.21]
normo_genes_sorted = sorted(normo_genes.items(), key=lambda x: x[1], reverse=True)
normo_genes_sorted # 538 genes overcome the mean threshold
[('"FTL"', 25689.24705882353),
('"CD44"', 11510.24705882353),
('"FTH1"', 11339.929411764706),
('"BEST1"', 9879.823529411764),
('"LDHA"', 8955.69411764706),
('"HSPA5"', 8251.588235294117),
('"MT-RNR2"', 7889.894117647059),
('"KRT19"', 6903.270588235294),
('"PSMD2"', 6743.4),
('"KRT18"', 6273.070588235294),
('"LAMC2"', 5719.164705882353),
('"MT-ND4"', 5480.964705882353),
('"LAMB3"', 5360.411764705882),
('"CD59"', 5279.7764705882355),
('"B2M"', 5247.623529411765),
('"MT-CO2"', 5157.517647058824),
('"ALDOA"', 4542.941176470588),
('"KRT8"', 4476.529411764706),
('"HSPA8"', 4437.917647058824),
('"HSP90B1"', 4296.529411764706),
('"ACTG1"', 3791.9882352941177),
('"CALM2"', 3691.247058823529),
('"PRDX1"', 3339.035294117647),
('"TUBB"', 3261.235294117647),
('"ANXA1"', 3196.470588235294),
('"UBC"', 3171.294117647059),
('"EZR"', 3146.1529411764704),
('"TUBA1B"', 3101.3176470588237),
('"SDHA"', 3089.3058823529414),
('"TMSB10"', 2957.329411764706),
('"LDHB"', 2859.4470588235295),
('"S100A2"', 2763.2705882352943),
('"TMSB4X"', 2710.9058823529413),
('"MT-CYB"', 2700.8117647058825),
('"UBB"', 2684.764705882353),
('"CAV1"', 2610.2),
('"MT-ND5"', 2599.858823529412),
('"MT-CO3"', 2563.258823529412),
('"TXN"', 2483.235294117647),
('"HMGA1"', 2434.3529411764707),
('"KRT7"', 2390.0235294117647),
('"MIF"', 2386.858823529412),
('"MIF-AS1"', 2380.3058823529414),
('"LMNA"', 2293.3058823529414),
('"CTSB"', 2261.2823529411767),
('"H2AZ1"', 2040.6941176470589),
('"MT-ND4L"', 2000.6941176470589),
('"TK1"', 1998.9882352941177),
('"PSAP"', 1984.835294117647),
('"SLC3A2"', 1975.5176470588235),
('"AKR1C2"', 1941.1882352941177),
('"P4HB"', 1887.1764705882354),
('"HSP90AA1"', 1799.0117647058823),
('"GPX3"', 1766.8235294117646),
('"CANX"', 1706.3882352941177),
('"NQO1"', 1665.8117647058823),
('"MALAT1"', 1651.2588235294118),
('"KRT6A"', 1590.5176470588235),
('"FOSL1"', 1576.9764705882353),
('"CDC20"', 1556.0235294117647),
('"TFRC"', 1550.7882352941176),
('"TUBB4B"', 1540.5058823529412),
('"STMN1"', 1510.929411764706),
('"KPNA2"', 1509.8588235294117),
('"SQSTM1"', 1504.2117647058824),
('"S100A16"', 1481.9529411764706),
('"PHLDA1"', 1457.5529411764705),
('"ACTN1"', 1437.270588235294),
('"PGK1"', 1430.070588235294),
('"F3"', 1409.3882352941177),
('"GAS6"', 1359.0470588235294),
('"AKR1C3"', 1334.3647058823528),
('"PLAU"', 1332.1529411764707),
('"ITGA6"', 1325.270588235294),
('"ADIRF-AS1"', 1314.2823529411764),
('"ADIRF"', 1274.035294117647),
('"H4C3"', 1232.2588235294118),
('"CCNB1"', 1211.870588235294),
('"GPI"', 1189.8823529411766),
('"SFN"', 1177.4470588235295),
('"EIF5"', 1170.6588235294118),
('"SCD"', 1169.9176470588236),
('"DHCR24"', 1160.8470588235293),
('"PXN"', 1155.129411764706),
('"SPP1"', 1141.2235294117647),
('"PRNP"', 1136.6705882352942),
('"FDFT1"', 1132.9058823529413),
('"SERPINB5"', 1122.129411764706),
('"MAL2"', 1116.7529411764706),
('"SEC61A1"', 1115.9176470588236),
('"LAMA3"', 1108.2823529411764),
('"KRT5"', 1079.7058823529412),
('"MT2A"', 1073.9529411764706),
('"TPX2"', 1071.2117647058824),
('"RRM2"', 1069.3647058823528),
('"GLUL"', 1050.2470588235294),
('"DKK1"', 1024.0823529411764),
('"GPRC5A"', 1017.7882352941176),
('"PLK2"', 997.7529411764706),
('"TACSTD2"', 991.7411764705882),
('"SLC1A5"', 989.4),
('"SRXN1"', 957.2823529411764),
('"PERP"', 945.8),
('"HSPH1"', 942.0470588235294),
('"S100A6"', 921.0588235294117),
('"MT-RNR1"', 917.8705882352941),
('"TINAGL1"', 914.0823529411765),
('"THBS1"', 888.1882352941177),
('"TXNRD1"', 871.5764705882353),
('"KRT14"', 871.0941176470589),
('"MCM3"', 867.4117647058823),
('"COL17A1"', 858.2823529411764),
('"DHCR7"', 840.1764705882352),
('"SRM"', 838.564705882353),
('"PYGB"', 834.8),
('"BIRC5"', 828.8705882352941),
('"C19orf48"', 827.435294117647),
('"GPX2"', 823.1058823529412),
('"JUP"', 812.2235294117647),
('"UCA1"', 802.7882352941176),
('"NFKBIA"', 791.8470588235294),
('"FLNB"', 786.8470588235294),
('"KDM1A"', 781.6823529411764),
('"LAMC1"', 779.9882352941177),
('"TUBB6"', 779.7764705882353),
('"PTHLH"', 773.5411764705882),
('"FLNA"', 773.4823529411765),
('"ACAT2"', 773.1764705882352),
('"MYH9"', 762.1764705882352),
('"HMGB2"', 754.7176470588236),
('"ST14"', 753.364705882353),
('"PHLDA1-AS1"', 742.0941176470589),
('"FJX1"', 737.9764705882353),
('"POP4"', 737.4),
('"KRT17"', 736.8),
('"CSTB"', 700.5058823529412),
('"GRN"', 697.035294117647),
('"MMP1"', 695.3764705882353),
('"MCM7"', 683.9294117647058),
('"AKR1B1"', 682.435294117647),
('"BLCAP"', 668.7411764705882),
('"AURKB"', 668.5058823529412),
('"MRNIP"', 665.5411764705882),
('"NUSAP1"', 656.7647058823529),
('"WARS1"', 656.6),
('"ZWINT"', 653.7647058823529),
('"LIPA"', 649.2117647058824),
('"SLC20A1"', 639.5411764705882),
('"NOLC1"', 637.5058823529412),
('"AKR1C1"', 636.0823529411765),
('"ARL6IP1"', 631.7529411764706),
('"PRSS23"', 631.035294117647),
('"SLC7A5"', 626.6470588235294),
('"CPA4"', 615.8588235294118),
('"EHF"', 614.0117647058823),
('"HLA-A"', 610.4470588235295),
('"FSCN1"', 604.0823529411765),
('"CDC6"', 595.1882352941177),
('"G0S2"', 594.635294117647),
('"JUNB"', 594.4823529411765),
('"SLC2A1"', 590.6823529411764),
('"PCDH1"', 578.0),
('"NET1"', 576.2705882352941),
('"CAVIN3"', 571.7294117647059),
('"MEST"', 570.6588235294117),
('"ATAD2"', 569.2941176470588),
('"SLC38A2"', 567.3176470588236),
('"TMBIM1"', 563.2705882352941),
('"PRC1"', 563.2),
('"SLC25A1"', 556.3764705882353),
('"ARNTL2"', 548.2941176470588),
('"IRF6"', 546.5294117647059),
('"UGDH"', 538.6117647058824),
('"FEN1"', 538.364705882353),
('"FXYD3"', 537.6588235294117),
('"PTP4A1"', 536.4705882352941),
('"ITGB4"', 529.8235294117648),
('"DDIT4"', 529.0117647058823),
('"MCM2"', 529.0),
('"IDI1"', 526.2588235294118),
('"PSAT1"', 526.2470588235294),
('"COPG1"', 524.7294117647059),
('"AMIGO2"', 512.635294117647),
('"TRIM16"', 507.90588235294115),
('"CTSD"', 502.9529411764706),
('"SQLE"', 500.6235294117647),
('"FAM83A"', 500.0705882352941),
('"CTNNAL1"', 499.94117647058823),
('"UBE2S"', 498.4823529411765),
('"MCM4"', 497.5764705882353),
('"ZFP36L1"', 497.45882352941175),
('"CCNB2"', 491.5176470588235),
('"UPK1B"', 491.05882352941177),
('"PCNA"', 487.49411764705883),
('"ID3"', 487.08235294117645),
('"FGFBP1"', 485.1411764705882),
('"MYBL2"', 478.52941176470586),
('"MKI67"', 476.5764705882353),
('"NUP188"', 475.4),
('"UBE2C"', 461.2470588235294),
('"UAP1"', 458.9529411764706),
('"CDK2AP2"', 457.0352941176471),
('"ANLN"', 453.4470588235294),
('"GFPT1"', 451.7294117647059),
('"POLR2A"', 450.21176470588233),
('"UGT1A6"', 450.01176470588234),
('"BAG3"', 447.29411764705884),
('"DCBLD2"', 447.21176470588233),
('"UGT1A10"', 446.3764705882353),
('"PTTG1"', 443.0),
('"DTL"', 439.4235294117647),
('"MET"', 437.5529411764706),
('"IER2"', 432.0),
('"UBE2T"', 423.38823529411764),
('"HK1"', 422.29411764705884),
('"KIF20A"', 421.36470588235295),
('"RNF26"', 415.4235294117647),
('"MCM5"', 413.98823529411766),
('"MSH6"', 413.1058823529412),
('"ID1"', 410.08235294117645),
('"CCNE1"', 408.74117647058824),
('"KIF23"', 408.7294117647059),
('"DSP"', 407.21176470588233),
('"IRAK1"', 404.7647058823529),
('"PPP1R15A"', 404.4470588235294),
('"CDKN1A"', 403.9529411764706),
('"TIMP3"', 399.98823529411766),
('"EFEMP1"', 393.52941176470586),
('"AURKA"', 393.2823529411765),
('"CDK1"', 393.15294117647056),
('"FOS"', 392.2705882352941),
('"SLC7A11"', 388.67058823529413),
('"BRD2"', 386.4470588235294),
('"RBM14"', 386.21176470588233),
('"CLDN4"', 385.1058823529412),
('"PPIF"', 385.05882352941177),
('"NCAPD2"', 383.8941176470588),
('"EPHA2"', 382.47058823529414),
('"AVPI1"', 380.15294117647056),
('"KNSTRN"', 378.4470588235294),
('"TRIM29"', 378.3764705882353),
('"CLDN7"', 378.2),
('"TRIB3"', 377.1294117647059),
('"IGFBP3"', 376.56470588235294),
('"KRT13"', 374.56470588235294),
('"PHGDH"', 372.45882352941175),
('"AARS1"', 372.0705882352941),
('"UNG"', 369.2470588235294),
('"KIF22"', 368.4470588235294),
('"LDLR"', 367.0),
('"MYC"', 365.25882352941176),
('"LRRC8A"', 365.15294117647056),
('"SEMA4B"', 364.2),
('"VARS1"', 361.9647058823529),
('"KIAA0040"', 361.6588235294118),
('"MCM10"', 361.3764705882353),
('"TOP2A"', 360.36470588235295),
('"ALDH1A3"', 356.38823529411764),
('"ANXA3"', 351.22352941176473),
('"KYNU"', 350.54117647058825),
('"CAVIN1"', 349.22352941176473),
('"SLCO4A1"', 344.8235294117647),
('"DUSP6"', 344.1411764705882),
('"SPAG5"', 339.0),
('"CDH1"', 337.2),
('"PFKL"', 336.0705882352941),
('"ERO1A"', 332.4823529411765),
('"CCNA2"', 330.9294117647059),
('"ABLIM1"', 326.6588235294118),
('"KIF2C"', 324.98823529411766),
('"SSR3"', 322.45882352941175),
('"BUB1B"', 320.1764705882353),
('"PLIN2"', 320.1411764705882),
('"AHNAK"', 317.8235294117647),
('"EIF4A2"', 315.50588235294117),
('"EMP1"', 314.4117647058824),
('"SERPINE2"', 312.6470588235294),
('"TNFRSF21"', 312.01176470588234),
('"TGFBI"', 308.50588235294117),
('"CKS2"', 307.8588235294118),
('"CALML5"', 307.3529411764706),
('"SMG7"', 307.3176470588235),
('"PLK1"', 306.0705882352941),
('"HERPUD1"', 304.88235294117646),
('"GAS6-AS1"', 303.4235294117647),
('"NUCB2"', 302.98823529411766),
('"CALB1"', 302.78823529411767),
('"ISG15"', 299.43529411764706),
('"TPBG"', 295.43529411764706),
('"POLD1"', 293.9764705882353),
('"RND3"', 292.6),
('"ALDH3A1"', 288.25882352941176),
('"GINS2"', 283.11764705882354),
('"CDC45"', 281.21176470588233),
('"SDC1"', 279.2705882352941),
('"COL4A2"', 278.3176470588235),
('"C10orf55"', 268.9294117647059),
('"FST"', 264.05882352941177),
('"CRKL"', 263.5529411764706),
('"NRP1"', 262.94117647058823),
('"PTGES"', 260.8705882352941),
('"PLEKHF1"', 252.95294117647057),
('"PPRC1"', 252.25882352941176),
('"ASF1B"', 249.0705882352941),
('"FAM83D"', 248.61176470588236),
('"CKAP2"', 248.4),
('"SAT1"', 247.28235294117647),
('"TGFBR2"', 243.8),
('"ITGB5"', 243.08235294117648),
('"MSMO1"', 241.75294117647059),
('"SHCBP1"', 240.72941176470587),
('"HJURP"', 240.2941176470588),
('"BCAR3"', 238.6705882352941),
('"BCAR1"', 236.7058823529412),
('"LVRN"', 234.90588235294118),
('"LIG1"', 234.4),
('"MELK"', 233.30588235294118),
('"CDKN3"', 233.25882352941176),
('"KIF11"', 232.88235294117646),
('"SNAI2"', 230.63529411764705),
('"UHRF1"', 230.52941176470588),
('"CEP55"', 229.38823529411764),
('"ITGA5"', 228.61176470588236),
('"KLF6"', 228.0),
('"BUB1"', 227.1529411764706),
('"DUSP1"', 224.76470588235293),
('"RFWD3"', 224.63529411764705),
('"TNFAIP2"', 222.95294117647057),
('"HSPB1"', 220.83529411764707),
('"MCM6"', 220.57647058823528),
('"GPNMB"', 219.63529411764705),
('"KLK5"', 217.31764705882352),
('"ANO1"', 216.98823529411766),
('"IL20RB"', 215.43529411764706),
('"PIMREG"', 214.95294117647057),
('"FSTL1"', 214.94117647058823),
('"PHF19"', 214.16470588235293),
('"KRT4"', 213.95294117647057),
('"AJUBA"', 213.88235294117646),
('"TMEM200A"', 212.5529411764706),
('"TOB1"', 211.47058823529412),
('"KLK10"', 211.43529411764706),
('"CCNE2"', 209.9294117647059),
('"PLAT"', 209.51764705882354),
('"CD55"', 206.8),
('"RRP12"', 206.05882352941177),
('"NCAPG"', 205.61176470588236),
('"PINK1"', 204.94117647058823),
('"AMOTL2"', 204.42352941176472),
('"CDCA8"', 204.23529411764707),
('"SMURF2"', 204.16470588235293),
('"HAS2"', 202.27058823529413),
('"FAM111A"', 202.09411764705882),
('"XDH"', 201.8235294117647),
('"PLAUR"', 200.81176470588235),
('"TM4SF1"', 200.8),
('"ALDH1A3-AS1"', 200.74117647058824),
('"ECT2"', 199.8235294117647),
('"TSC22D1"', 196.88235294117646),
('"TCF19"', 196.8235294117647),
('"ITGA2"', 196.35294117647058),
('"CDH13"', 195.52941176470588),
('"CYP51A1"', 194.96470588235294),
('"SERPINE1"', 194.12941176470588),
('"NID2"', 190.58823529411765),
('"NDC80"', 190.5529411764706),
('"CENPF"', 188.95294117647057),
('"SYTL3"', 188.2),
('"SERPINB2"', 188.12941176470588),
('"CD74"', 187.95294117647057),
('"CYP1B1"', 187.72941176470587),
('"FADS1"', 184.54117647058823),
('"CENPA"', 184.08235294117648),
('"HMGCR"', 183.3764705882353),
('"EPDR1"', 182.52941176470588),
('"NUMA1"', 181.65882352941176),
('"P4HA1"', 179.23529411764707),
('"TYSND1"', 178.03529411764706),
('"EXO1"', 177.28235294117647),
('"CDCA3"', 177.27058823529413),
('"SEC24D"', 175.50588235294117),
('"FYB1"', 174.04705882352943),
('"CCNF"', 172.85882352941175),
('"VEGFA"', 172.5529411764706),
('"DUSP10"', 171.94117647058823),
('"GYS1"', 170.8470588235294),
('"LINC01764"', 170.63529411764705),
('"PXDN"', 170.61176470588236),
('"GLTP"', 168.63529411764705),
('"NECTIN2"', 167.6),
('"KLF10"', 167.2),
('"NEK2"', 165.63529411764705),
('"GADD45A"', 165.16470588235293),
('"FHL1"', 162.94117647058823),
('"FAM162A"', 162.85882352941175),
('"PPARG"', 162.4),
('"SLC25A19"', 162.2941176470588),
('"RECQL4"', 161.2235294117647),
('"DLGAP5"', 158.98823529411766),
('"NEU1"', 158.72941176470587),
('"SLC7A8"', 158.38823529411764),
('"LSS"', 158.1529411764706),
('"SLC43A3"', 157.9764705882353),
('"IFIT2"', 157.23529411764707),
('"RIPK4"', 157.1529411764706),
('"KIF18A"', 156.78823529411764),
('"RBCK1"', 156.68235294117648),
('"VSIR"', 155.08235294117648),
('"PFKFB3"', 154.04705882352943)]
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_summary.transpose()
normoxia_summary_t = normoxia_summary.transpose()
# Calculate the difference in means
diff_mean = abs(hypoxia_summary_t['mean'] - normoxia_summary_t['mean'])
# Sort genes by difference in mean in descending order
sorted_genes = diff_mean.sort_values(ascending=False)
# Get the list of genes
genes_list = sorted_genes.index.tolist()
genes_list
# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes.reset_index()
# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']
# Display the DataFrame
sorted_genes_df[:50]
| Gene | Difference | |
|---|---|---|
| 0 | "LDHA" | 15099.687326 |
| 1 | "KRT19" | 6694.110855 |
| 2 | "DDIT4" | 6585.256277 |
| 3 | "ALDOA" | 5756.728927 |
| 4 | "FTL" | 4227.133657 |
| 5 | "PGK1" | 3859.702608 |
| 6 | "FTH1" | 2527.503578 |
| 7 | "ANGPTL4" | 2214.859673 |
| 8 | "BEST1" | 2189.887811 |
| 9 | "HSP90B1" | 2002.769557 |
| 10 | "HSPA5" | 1991.525167 |
| 11 | "SLC2A1" | 1866.286719 |
| 12 | "KRT18" | 1687.926258 |
| 13 | "CD44" | 1683.999636 |
| 14 | "IGFBP3" | 1678.744572 |
| 15 | "TMSB10" | 1646.371619 |
| 16 | "SCD" | 1569.659673 |
| 17 | "PLIN2" | 1484.343360 |
| 18 | "GPI" | 1432.890843 |
| 19 | "HSPA8" | 1394.031049 |
| 20 | "PSMD2" | 1366.606186 |
| 21 | "MIF" | 1243.419527 |
| 22 | "MIF-AS1" | 1239.786901 |
| 23 | "UBC" | 1205.241965 |
| 24 | "FAM83A" | 1118.568587 |
| 25 | "LAMB3" | 1072.629472 |
| 26 | "LDHB" | 1012.735719 |
| 27 | "AKR1C2" | 1000.652153 |
| 28 | "BNIP3" | 994.779381 |
| 29 | "KRT13" | 982.156944 |
| 30 | "GPRC5A" | 962.397332 |
| 31 | "FOS" | 946.533535 |
| 32 | "P4HB" | 875.400849 |
| 33 | "UCA1" | 870.943723 |
| 34 | "ERO1A" | 849.198059 |
| 35 | "FDFT1" | 816.898241 |
| 36 | "CSTB" | 806.535355 |
| 37 | "NDRG1" | 798.352577 |
| 38 | "AKR1C3" | 769.447180 |
| 39 | "F3" | 750.859187 |
| 40 | "DHCR7" | 741.988478 |
| 41 | "BLCAP" | 736.062947 |
| 42 | "TMBIM1" | 717.925288 |
| 43 | "UBB" | 710.218314 |
| 44 | "UPK1B" | 699.415403 |
| 45 | "MT-CYB" | 679.914857 |
| 46 | "SQLE" | 677.623893 |
| 47 | "EIF5" | 670.576349 |
| 48 | "TXN" | 669.462098 |
| 49 | "BHLHE40" | 657.776956 |
## Now with the std
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_summary.transpose()
normoxia_summary_t = normoxia_summary.transpose()
# Calculate the difference in means
diff_std = abs(hypoxia_summary_t['std'] - normoxia_summary_t['std'])
# Sort genes by difference in mean in descending order
sorted_genes_std = diff_std.sort_values(ascending=False)
# Get the list of genes
genes_list = sorted_genes_std.index.tolist()
genes_list
# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes_std.reset_index()
# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']
# Display the DataFrame
sorted_genes_df[:50]
| Gene | Difference | |
|---|---|---|
| 0 | "KRT17" | 6005.431853 |
| 1 | "FTL" | 5824.303256 |
| 2 | "FTH1" | 4410.586345 |
| 3 | "KRT19" | 4279.691662 |
| 4 | "LDHA" | 4126.866651 |
| 5 | "BEST1" | 3882.287729 |
| 6 | "KRT13" | 3765.976846 |
| 7 | "IGFBP3" | 2896.064265 |
| 8 | "KRT6A" | 2894.625380 |
| 9 | "DDIT4" | 2540.381481 |
| 10 | "HSPA5" | 2122.250614 |
| 11 | "ANGPTL4" | 2025.504784 |
| 12 | "MT-RNR2" | 1879.445668 |
| 13 | "KRT16" | 1875.826067 |
| 14 | "CEACAM5" | 1874.970276 |
| 15 | "KRT14" | 1720.208063 |
| 16 | "GLTP" | 1481.139986 |
| 17 | "CSTB" | 1305.217655 |
| 18 | "ALDOA" | 1249.288517 |
| 19 | "SPP1" | 1246.200083 |
| 20 | "PLIN2" | 1240.556021 |
| 21 | "AKR1C2" | 1230.482103 |
| 22 | "MMP1" | 1189.675202 |
| 23 | "KRT4" | 1149.773077 |
| 24 | "SLC3A2" | 1139.425636 |
| 25 | "KRT18" | 1135.719348 |
| 26 | "HSP90B1" | 1110.564728 |
| 27 | "PGK1" | 1083.055101 |
| 28 | "AKR1C3" | 1066.759828 |
| 29 | "FAM83A" | 1060.802868 |
| 30 | "JUP" | 971.988308 |
| 31 | "SULT2B1" | 964.045317 |
| 32 | "UCA1" | 925.114259 |
| 33 | "SCD" | 911.213323 |
| 34 | "UPK1B" | 865.824957 |
| 35 | "TMBIM1" | 834.626148 |
| 36 | "SLC2A1" | 829.434861 |
| 37 | "PRDX1" | 807.772448 |
| 38 | "LAMB3" | 790.156340 |
| 39 | "AKR1C1" | 774.892078 |
| 40 | "SPRR1B" | 757.519035 |
| 41 | "ANXA1" | 701.274437 |
| 42 | "GPRC5A" | 679.499106 |
| 43 | "HSPB1" | 670.564692 |
| 44 | "PSMD2" | 668.017664 |
| 45 | "CCNB1" | 656.644089 |
| 46 | "GPI" | 640.737191 |
| 47 | "KRT7" | 622.655000 |
| 48 | "UBC" | 619.232557 |
| 49 | "TMSB10" | 610.370935 |
#i want to do a clustering considering only genes: GAPDH, ALDOA and PGK1 as principal components
# Create a DataFrame with only the selected genes
selected_genes = ['"LDHA"', '"ALDOA"', '"PGK1"']
selected_genes_df = df_HCC1806_norm_filt.loc[selected_genes]
# Transpose the DataFrame
selected_genes_df = selected_genes_df.T
# Apply PCA to the selected genes
pca = PCA(n_components=3)
selected_genes_transformed = pca.fit_transform(selected_genes_df)
# Apply K-means clustering to the transformed data
kmeans = KMeans(n_clusters=2, random_state=42)
clusters = kmeans.fit_predict(selected_genes_transformed)
# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue'}
colors = conditions.map(color_map)
# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=colors)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('PCA of Selected Genes (LDHA, ALDOA, PGK1) Colored by Condition')
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1. warnings.warn(
# now we want to do a k-means clustering with the selected genes
# Apply K-means clustering to the transformed data
kmeans = KMeans(n_clusters=2, random_state=42)
clusters1 = kmeans.fit_predict(selected_genes_transformed)
# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypoxia': 'green', 'Normoxia': 'yellow'}
colors = conditions.map(color_map)
cluster_colorsH = ['yellow' if label == 0 else 'green' for label in clusters1]
# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=cluster_colorsH)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('K-means Clustering of Selected Genes (GAPDH, ALDOA, PGK1)')
plt.show()
#i want to see the match rate of the clustering
# Assign numeric labels based on conditions
###condition_numeric = conditions.map({'Normoxia': 0, 'Hypoxia': 1})
# Compute the confusion matrix
###cm = confusion_matrix(condition_numeric, clusters1)
# Calculate the match rate using the trace of the confusion matrix
###match_rate = np.trace(cm) / np.sum(cm)
###print(f'Match Rate: {match_rate:.2%}')
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1. warnings.warn(
Looking again at GAPDH, ALDOA and PGK1, we can again observe that these genes are some of the most useful in order to understand the condition of the cells. Although the result for the MCF7 cells was even more conclusive, the tests performed here show the relevance of these genes for the HCC1806 cells as well.
¶
2.2.9 More unsupervised learning
X_HCC_SMART = df_trans_HCC_SMART.drop('TARGET', axis=1)
y_train_HCC_SMART=df_trans_HCC_SMART['TARGET']
scaler_HCC_SMART = StandardScaler()
X_scaled_HCC_SMART = scaler_HCC_SMART.fit_transform(X_HCC_SMART)
pca2_HCC_SMART = PCA(n_components=2)
X_pca2_HCC_SMART = pca2_HCC_SMART.fit_transform(X_scaled_HCC_SMART)
plt.figure(figsize=(12, 6))
plt.scatter(X_pca2_HCC_SMART[:,0][y_train_HCC_SMART==1], X_pca2_HCC_SMART[:,1][y_train_HCC_SMART==1], c='r')
plt.scatter(X_pca2_HCC_SMART[:,0][y_train_HCC_SMART==0], X_pca2_HCC_SMART[:,1][y_train_HCC_SMART==0], c='b')
plt.title("Clusterization PCA HCC_SMART")
plt.show()
'''% of EXPLAINED VARIANCE'''
pca_HCC_SMART = PCA().fit(X_scaled_HCC_SMART)
explained_variance_ratio_HCC_SMART = pca_HCC_SMART.explained_variance_ratio_
cumulative_variance_HCC_SMART = explained_variance_ratio_HCC_SMART.cumsum()
plt.plot(range(1, len(cumulative_variance_HCC_SMART) + 1), cumulative_variance_HCC_SMART, marker='o')
plt.xlabel("Number of components")
plt.ylabel("Cumulative proportion of Variance Explained")
plt.title("Analysis of explained variance")
plt.show()
# TSNE
tsne_model_HCC_SMART = TSNE(n_components=2) #, perplexity=10
X_tsne_HCC_SMART = tsne_model_HCC_SMART.fit_transform(X_scaled_HCC_SMART)
# Visualization of results
plt.figure(figsize=(12, 6))
# plt.scatter(X_train_pca[:,0], X_train_pca[:,1], cmap='viridis')
plt.scatter(X_tsne_HCC_SMART[:,0][y_train_HCC_SMART==1], X_tsne_HCC_SMART[:,1][y_train_HCC_SMART==1], c='r')
plt.scatter(X_tsne_HCC_SMART[:,0][y_train_HCC_SMART==0], X_tsne_HCC_SMART[:,1][y_train_HCC_SMART==0], c='b')
plt.title("Clusterization TSNE HCC_SMART")
plt.show()
¶
- Analysis on DropSeq and Unsupervided/Supervised learning on DropSeq
¶
3.1) MCF7
¶
3.1.0) OPENING THE MAIN DATASET: MCF7 DATASET PRODUCED USING THE DROPSEQ TECHNOLOGY
We start now opening the MCF7 DropSeq filtered normalized dataset since we are interested in analyzing it.
# The dataset filtered is useful to apply statistical tools
df_MCF7_filtered = pd.read_csv("DropSeq/MCF7_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)
print("Dataframe dimensions:", np.shape(df_MCF7_filtered))
print("First column: ", df_MCF7_filtered.iloc[ : , :])
Dataframe dimensions: (3000, 21626)
First column: "AAAAACCTATCG_Normoxia" "AAAACAACCCTA_Normoxia" \
"MALAT1" 1 3
"MT-RNR2" 0 0
"NEAT1" 0 0
"H1-5" 0 0
"TFF1" 4 1
... ... ...
"BRWD1-AS2" 0 0
"RPS19BP1" 0 0
"AUNIP" 0 0
"TNK2" 0 0
"SUDS3" 0 0
"AAAACACTCTCA_Normoxia" "AAAACCAGGCAC_Normoxia" \
"MALAT1" 3 6
"MT-RNR2" 0 2
"NEAT1" 0 0
"H1-5" 0 0
"TFF1" 1 1
... ... ...
"BRWD1-AS2" 0 0
"RPS19BP1" 0 0
"AUNIP" 0 0
"TNK2" 0 0
"SUDS3" 0 0
"AAAACCTAGCTC_Normoxia" "AAAACCTCCGGG_Normoxia" \
"MALAT1" 4 5
"MT-RNR2" 0 0
"NEAT1" 0 2
"H1-5" 0 2
"TFF1" 0 0
... ... ...
"BRWD1-AS2" 0 0
"RPS19BP1" 0 0
"AUNIP" 0 0
"TNK2" 0 0
"SUDS3" 0 0
"AAAACTCGTTGC_Normoxia" "AAAAGAGCTCTC_Normoxia" \
"MALAT1" 1 13
"MT-RNR2" 2 1
"NEAT1" 0 1
"H1-5" 0 0
"TFF1" 0 2
... ... ...
"BRWD1-AS2" 0 0
"RPS19BP1" 0 0
"AUNIP" 0 0
"TNK2" 0 0
"SUDS3" 0 0
"AAAAGCTAGGCG_Normoxia" "AAAATCGCATTT_Normoxia" ... \
"MALAT1" 3 3 ...
"MT-RNR2" 7 0 ...
"NEAT1" 2 0 ...
"H1-5" 0 0 ...
"TFF1" 0 1 ...
... ... ... ...
"BRWD1-AS2" 0 0 ...
"RPS19BP1" 0 0 ...
"AUNIP" 0 0 ...
"TNK2" 0 0 ...
"SUDS3" 0 0 ...
"TTTTACAGGATC_Hypoxia" "TTTTACCACGTA_Hypoxia" \
"MALAT1" 0 2
"MT-RNR2" 0 0
"NEAT1" 0 0
"H1-5" 0 1
"TFF1" 2 3
... ... ...
"BRWD1-AS2" 0 0
"RPS19BP1" 0 0
"AUNIP" 0 0
"TNK2" 0 0
"SUDS3" 0 0
"TTTTATGCTACG_Hypoxia" "TTTTCCAGACGC_Hypoxia" \
"MALAT1" 1 0
"MT-RNR2" 0 0
"NEAT1" 0 0
"H1-5" 0 0
"TFF1" 8 0
... ... ...
"BRWD1-AS2" 0 0
"RPS19BP1" 0 0
"AUNIP" 0 0
"TNK2" 0 0
"SUDS3" 0 0
"TTTTCGCGCTCG_Hypoxia" "TTTTCGCGTAGA_Hypoxia" \
"MALAT1" 1 0
"MT-RNR2" 0 0
"NEAT1" 0 0
"H1-5" 1 0
"TFF1" 0 3
... ... ...
"BRWD1-AS2" 0 0
"RPS19BP1" 0 0
"AUNIP" 0 0
"TNK2" 0 0
"SUDS3" 0 0
"TTTTCGTCCGCT_Hypoxia" "TTTTCTCCGGCT_Hypoxia" \
"MALAT1" 1 0
"MT-RNR2" 0 0
"NEAT1" 0 0
"H1-5" 0 1
"TFF1" 4 2
... ... ...
"BRWD1-AS2" 0 0
"RPS19BP1" 0 0
"AUNIP" 0 0
"TNK2" 0 0
"SUDS3" 0 1
"TTTTGTTCAAAG_Hypoxia" "TTTTTTGTATGT_Hypoxia"
"MALAT1" 0 4
"MT-RNR2" 0 0
"NEAT1" 0 0
"H1-5" 0 0
"TFF1" 6 0
... ... ...
"BRWD1-AS2" 0 0
"RPS19BP1" 0 0
"AUNIP" 0 0
"TNK2" 0 0
"SUDS3" 0 0
[3000 rows x 21626 columns]
df_MCF7_filtered.head()
| "AAAAACCTATCG_Normoxia" | "AAAACAACCCTA_Normoxia" | "AAAACACTCTCA_Normoxia" | "AAAACCAGGCAC_Normoxia" | "AAAACCTAGCTC_Normoxia" | "AAAACCTCCGGG_Normoxia" | "AAAACTCGTTGC_Normoxia" | "AAAAGAGCTCTC_Normoxia" | "AAAAGCTAGGCG_Normoxia" | "AAAATCGCATTT_Normoxia" | ... | "TTTTACAGGATC_Hypoxia" | "TTTTACCACGTA_Hypoxia" | "TTTTATGCTACG_Hypoxia" | "TTTTCCAGACGC_Hypoxia" | "TTTTCGCGCTCG_Hypoxia" | "TTTTCGCGTAGA_Hypoxia" | "TTTTCGTCCGCT_Hypoxia" | "TTTTCTCCGGCT_Hypoxia" | "TTTTGTTCAAAG_Hypoxia" | "TTTTTTGTATGT_Hypoxia" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "MALAT1" | 1 | 3 | 3 | 6 | 4 | 5 | 1 | 13 | 3 | 3 | ... | 0 | 2 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 4 |
| "MT-RNR2" | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 1 | 7 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "NEAT1" | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 2 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "H1-5" | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| "TFF1" | 4 | 1 | 1 | 1 | 0 | 0 | 0 | 2 | 0 | 1 | ... | 2 | 3 | 8 | 0 | 0 | 3 | 4 | 2 | 6 | 0 |
5 rows × 21626 columns
df_MCF7_filtered.shape
(3000, 21626)
The MCF7 DropSeq dataset is made of 3000 rows representing specific genes and 21626 columns representing the RNA strands. Therefore each entry tells us how much of every gene is expressed in every RNA strand. Moreover, each RNA strand has in its name written the state in which it was under (normoxia / hypoxia).
¶
3.1.1) SPARSITY OF THE DATA
Since the data is already filtered and normalized, we can directy proceed on seeing how many zeros we have and then compute the percentage of sparcity.
# We proceed in the following way. We start representing the dataframe as a simple matrix to see how many zero we have
# then we compute the percentage of the sparsity on our dataset doing 1 - number_of_non_zero_genes / total_number of genes
df_MCF7_filtered_Matrix = np.array(df_MCF7_filtered)
print("Matrix useful for the sparsity check: \n", df_MCF7_filtered_Matrix)
Matrix useful for the sparsity check: [[1 3 3 ... 0 0 4] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] ... [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 1 0 0]]
Sparsity_matrix_MCF7 = 1 - count_nonzero(df_MCF7_filtered_Matrix)/ df_MCF7_filtered_Matrix.size
Sparsity_matrix_MCF7
0.9752943062363205
As we can see the percentage of sparcity is extremely high. This is not something unexpected as the RNA strands are small and contain only few genes.
This leads us to skip also the outliers removal as the only genes that are expressed would in this case be detected as outliers and then we would lose all the informations.
¶
3.1.2) DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS
We transpose the dataset since having the genes in the colums and the RNA strands in the rows is more intresting under a statistical viewpoint.
df_MCF7_filtered_T = df_MCF7_filtered.T
df_MCF7_filtered_T
| "MALAT1" | "MT-RNR2" | "NEAT1" | "H1-5" | "TFF1" | "MT-RNR1" | "H4C3" | "GDF15" | "KRT81" | "MT-CO3" | ... | "MROH1" | "SKIDA1" | "MICALL1" | "RARG" | "MYO1F" | "BRWD1-AS2" | "RPS19BP1" | "AUNIP" | "TNK2" | "SUDS3" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "AAAAACCTATCG_Normoxia" | 1 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "AAAACAACCCTA_Normoxia" | 3 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "AAAACACTCTCA_Normoxia" | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "AAAACCAGGCAC_Normoxia" | 6 | 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "AAAACCTAGCTC_Normoxia" | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| "TTTTCGCGTAGA_Hypoxia" | 0 | 0 | 0 | 0 | 3 | 0 | 7 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "TTTTCGTCCGCT_Hypoxia" | 1 | 0 | 0 | 0 | 4 | 0 | 1 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "TTTTCTCCGGCT_Hypoxia" | 0 | 0 | 0 | 1 | 2 | 0 | 4 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| "TTTTGTTCAAAG_Hypoxia" | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "TTTTTTGTATGT_Hypoxia" | 4 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
21626 rows × 3000 columns
df_MCF7_filtered_T_summary1 = df_MCF7_filtered_T.describe()
df_MCF7_filtered_T_summary1
| "MALAT1" | "MT-RNR2" | "NEAT1" | "H1-5" | "TFF1" | "MT-RNR1" | "H4C3" | "GDF15" | "KRT81" | "MT-CO3" | ... | "MROH1" | "SKIDA1" | "MICALL1" | "RARG" | "MYO1F" | "BRWD1-AS2" | "RPS19BP1" | "AUNIP" | "TNK2" | "SUDS3" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | ... | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 | 21626.000000 |
| mean | 3.634791 | 0.850458 | 0.136086 | 0.368862 | 1.799362 | 0.291362 | 1.730972 | 0.011884 | 0.026912 | 0.105105 | ... | 0.008046 | 0.002035 | 0.003930 | 0.016138 | 0.000971 | 0.000832 | 0.034172 | 0.002405 | 0.003884 | 0.088042 |
| std | 6.837353 | 1.721088 | 0.643060 | 0.953887 | 2.486034 | 0.740664 | 2.328460 | 0.189425 | 0.233430 | 0.387558 | ... | 0.092892 | 0.048995 | 0.065461 | 0.131046 | 0.032598 | 0.028839 | 0.200548 | 0.053491 | 0.064395 | 0.323733 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 4.000000 | 1.000000 | 0.000000 | 0.000000 | 3.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 175.000000 | 32.000000 | 23.000000 | 22.000000 | 46.000000 | 16.000000 | 44.000000 | 20.000000 | 17.000000 | 5.000000 | ... | 2.000000 | 3.000000 | 2.000000 | 3.000000 | 2.000000 | 1.000000 | 4.000000 | 3.000000 | 2.000000 | 3.000000 |
8 rows × 3000 columns
We can see that the mean of most genes is across zero. This was predictable as the sparcity analysis told us that the 97% of the entries is 0.
We then plot the 25/50/75% and we can clearly see that the non-zero genes are almost all located in the 75% quartile.
#we make a histogram of the mean of the expression of the genes
sns.displot(df_MCF7_filtered_T_summary1.loc['mean'], bins=20, kde=True)
plt.xlabel('Mean of the expression of the genes')
plt.ylabel('Frequency')
plt.title('Histogram of the mean of the expression of the genes')
plt.show()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
#we look only at the 25% quantile
sns.displot(df_MCF7_filtered_T_summary1.loc['25%'], bins=20, kde=True)
plt.xlabel('25% quantile of the expression of the genes')
plt.ylabel('Frequency')
plt.title('Histogram of the 25% quantile of the expression of the genes')
plt.show()
df_MCF7_filtered_T_summary1.loc['25%'].value_counts()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
25% 0.0 2998 1.0 2 Name: count, dtype: int64
#we look only at the 50% quantile
sns.displot(df_MCF7_filtered_T_summary1.loc['50%'], bins=20, kde=True)
plt.xlabel('50% quantile of the expression of the genes')
plt.ylabel('Frequency')
plt.title('Histogram of the 50% quantile of the expression of the genes')
plt.show()
df_MCF7_filtered_T_summary1.loc['50%'].value_counts()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
50% 0.0 2985 1.0 12 2.0 2 3.0 1 Name: count, dtype: int64
#we look only at the 75% quantile
sns.displot(df_MCF7_filtered_T_summary1.loc['75%'], bins=20, kde=True)
plt.xlabel('75% quantile of the expression of the genes')
plt.ylabel('Frequency')
plt.title('Histogram of the 75% quantile of the expression of the genes')
plt.show()
df_MCF7_filtered_T_summary1.loc['75%'].value_counts()
c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
75% 0.0 2934 1.0 51 2.0 11 4.0 2 3.0 1 5.0 1 Name: count, dtype: int64
¶
3.1.3) LOOKING FOR MOST USEFUL AND EXPRESSED GENES ACROSS CELLS
Since the mean of most of the genes as seen before is near zero, we restrict our search to the genes that have at least a mean expression of 1.
Also for what regards the standard deviation we restrict our search to the genes that have a standard deviation higher 1.
We split the dataset into two parts containing normoxic RNA strands and hypoxic RNA strands.
#i want to split the dataset in 2 parts, one with RNA with hypoxia in the name and the other with RNA with normoxia in the name
hypoxia_samples = df_MCF7_filtered.T[df_MCF7_filtered.T.index.str.contains('Hypoxia')]
normoxia_samples = df_MCF7_filtered.T[df_MCF7_filtered.T.index.str.contains('Normoxia')]
hypoxia_samples.describe()
| "MALAT1" | "MT-RNR2" | "NEAT1" | "H1-5" | "TFF1" | "MT-RNR1" | "H4C3" | "GDF15" | "KRT81" | "MT-CO3" | ... | "MROH1" | "SKIDA1" | "MICALL1" | "RARG" | "MYO1F" | "BRWD1-AS2" | "RPS19BP1" | "AUNIP" | "TNK2" | "SUDS3" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | ... | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 | 8921.000000 |
| mean | 2.367672 | 0.097523 | 0.158727 | 0.302096 | 3.293241 | 0.014684 | 2.260285 | 0.021522 | 0.034189 | 0.010201 | ... | 0.006502 | 0.001457 | 0.003139 | 0.009976 | 0.001121 | 0.000560 | 0.033404 | 0.001794 | 0.003699 | 0.073198 |
| std | 7.290005 | 0.432320 | 0.836743 | 0.861408 | 3.071657 | 0.140896 | 2.980723 | 0.282102 | 0.306976 | 0.111085 | ... | 0.081757 | 0.038148 | 0.057908 | 0.100510 | 0.036661 | 0.023669 | 0.190010 | 0.044886 | 0.060711 | 0.289425 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 5.000000 | 0.000000 | 3.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 175.000000 | 11.000000 | 23.000000 | 15.000000 | 46.000000 | 4.000000 | 44.000000 | 20.000000 | 17.000000 | 4.000000 | ... | 2.000000 | 1.000000 | 2.000000 | 2.000000 | 2.000000 | 1.000000 | 3.000000 | 2.000000 | 1.000000 | 3.000000 |
8 rows × 3000 columns
normoxia_samples.describe()
| "MALAT1" | "MT-RNR2" | "NEAT1" | "H1-5" | "TFF1" | "MT-RNR1" | "H4C3" | "GDF15" | "KRT81" | "MT-CO3" | ... | "MROH1" | "SKIDA1" | "MICALL1" | "RARG" | "MYO1F" | "BRWD1-AS2" | "RPS19BP1" | "AUNIP" | "TNK2" | "SUDS3" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | ... | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 | 12705.000000 |
| mean | 4.524518 | 1.379142 | 0.120189 | 0.415742 | 0.750413 | 0.485636 | 1.359307 | 0.005116 | 0.021802 | 0.171743 | ... | 0.009130 | 0.002440 | 0.004486 | 0.020464 | 0.000866 | 0.001023 | 0.034711 | 0.002834 | 0.004014 | 0.098465 |
| std | 6.351683 | 2.057514 | 0.460112 | 1.011196 | 1.108189 | 0.910149 | 1.629650 | 0.071346 | 0.162862 | 0.486050 | ... | 0.099961 | 0.055353 | 0.070278 | 0.148640 | 0.029413 | 0.031973 | 0.207634 | 0.058783 | 0.066863 | 0.345425 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 2.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 6.000000 | 2.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 131.000000 | 32.000000 | 11.000000 | 22.000000 | 9.000000 | 16.000000 | 39.000000 | 1.000000 | 3.000000 | 5.000000 | ... | 2.000000 | 3.000000 | 2.000000 | 3.000000 | 1.000000 | 1.000000 | 4.000000 | 3.000000 | 2.000000 | 3.000000 |
8 rows × 3000 columns
#we look at how many genes have a mean of expression greater than 1 and we print their names
#for hypoxia samples
hypoxia_samples_summary = hypoxia_samples.describe()
hypo_genes = hypoxia_samples_summary.loc['mean'][hypoxia_samples_summary.loc['mean'] > 1]
hypo_genes_sorted = sorted(hypo_genes.items(), key=lambda x: x[1], reverse=True)
print(hypo_genes_sorted)
print(f"{len(hypo_genes_sorted)} genes have a mean of expression greater than 1 in hypoxia samples")
## Now we apply the same with the standard deviation
hypo_genes_std = hypoxia_samples_summary.loc['std'][hypoxia_samples_summary.loc['std'] > 1]
hypo_genes_std_sorted = sorted(hypo_genes_std.items(), key=lambda x: x[1], reverse=True)
print(hypo_genes_std_sorted)
print(f"{len(hypo_genes_std_sorted)} genes have a standard deviation greater than 1 in hypoxia samples")
[('"RPS19"', 4.0482008743414415), ('"BCYRN1"', 3.5211299181706086), ('"TFF1"', 3.2932406680865376), ('"RPL13"', 2.500616522811344), ('"MALAT1"', 2.367671785674252), ('"RPL35"', 2.3223853828046184), ('"H4C3"', 2.2602847214437842), ('"RPS14"', 2.1470687142697007), ('"RPLP1"', 1.8000224190113217), ('"RPL12"', 1.6857975563277658), ('"RPS27"', 1.6446586705526287), ('"RPS12"', 1.5113776482457124), ('"HSPB1"', 1.467548481111983), ('"KRT8"', 1.4604864925456786), ('"RPL35A"', 1.340881067144939), ('"RPL34"', 1.3203676717856743), ('"TMSB10"', 1.2384261854052236), ('"RPLP2"', 1.184396368120166), ('"RPS5"', 1.1486380450622127), ('"RPL15"', 1.1452751933639727), ('"RPL39"', 1.1373164443448045), ('"RPL36"', 1.1111982961551397), ('"SERF2"', 1.1023427866831073), ('"RPS15A"', 1.0842954825692186), ('"RPS16"', 1.06064342562493), ('"KRT19"', 1.0517879161528976), ('"KRT18"', 1.0284721443784328), ('"RPS28"', 1.0112095056608004)]
28 genes have a mean of expression greater than 1 in hypoxia samples
[('"MALAT1"', 7.290005109760247), ('"BCYRN1"', 3.7218640607052977), ('"TFF1"', 3.071656963858318), ('"H4C3"', 2.9807231733949235), ('"RPS19"', 2.926860177044239), ('"RPL13"', 1.9134362829982945), ('"RPL35"', 1.9125154133162319), ('"RPS14"', 1.680441627534648), ('"RPL12"', 1.5961799461360446), ('"RPS12"', 1.5692210218211629), ('"RPLP1"', 1.5016896311743007), ('"RPS27"', 1.4918150374677128), ('"KRT8"', 1.3797998769815072), ('"TMSB10"', 1.3764461414196563), ('"HSPB1"', 1.3746934140582237), ('"RPS5"', 1.3612554361175306), ('"RPL35A"', 1.331170827023664), ('"RPLP2"', 1.2815379272648442), ('"RPL39"', 1.2767666629509271), ('"RPL34"', 1.257372393388114), ('"RPL36"', 1.2223484752177272), ('"RPS16"', 1.2191572434117108), ('"RPS28"', 1.2024302301824568), ('"RPS15A"', 1.2003647519785692), ('"KRT18"', 1.163956454202145), ('"ACTB"', 1.153635928043784), ('"DSCAM-AS1"', 1.1517097320184762), ('"RPL15"', 1.1476102753562882), ('"SERF2"', 1.1372987939015213), ('"LGALS1"', 1.1367763145299683), ('"S100A10"', 1.136580121061228), ('"FTL"', 1.1301379022905567), ('"KRT19"', 1.0725934572437787), ('"UQCRQ"', 1.0659433638098892), ('"RPS15"', 1.0621915646477988), ('"ELOB"', 1.0372161788541507), ('"RPL37A"', 1.0214144440509616)]
37 genes have a standard deviation greater than 1 in hypoxia samples
We can see that in hypoxic RNA strands most of the genes that have a mean higher than 1 have also the standard deviation higher than 1.
normoxia_samples_summary = normoxia_samples.describe()
normo_genes = normoxia_samples_summary.loc['mean'][normoxia_samples_summary.loc['mean'] > 1]
normo_genes_sorted = sorted(normo_genes.items(), key=lambda x: x[1], reverse=True)
print(normo_genes_sorted)
print(f" {len(normo_genes_sorted)} genes have a mean of expression greater than 92.814 in normoxia samples")
normo_genes_std = normoxia_samples_summary.loc['std'][normoxia_samples_summary.loc['std'] > 1]
normo_genes_std_sorted = sorted(normo_genes_std.items(), key=lambda x: x[1], reverse=True)
print(normo_genes_std_sorted)
print(f"{len(normo_genes_std_sorted)} genes have a standard deviation greater than 75.526 in normoxia samples")
[('"MALAT1"', 4.524517906336088), ('"BCYRN1"', 3.188902007083825), ('"RPS19"', 1.6436835891381345), ('"ACTB"', 1.486107831562377), ('"MT-RNR2"', 1.379142070051161), ('"H4C3"', 1.3593073593073592), ('"SET"', 1.0544667453758363)]
7 genes have a mean of expression greater than 92.814 in normoxia samples
[('"MALAT1"', 6.35168300075444), ('"BCYRN1"', 2.6682761014979333), ('"MT-RNR2"', 2.0575143475434845), ('"RPS19"', 1.6724908743586877), ('"H4C3"', 1.6296499520005536), ('"ACTB"', 1.4974211151986756), ('"KRT8"', 1.3604395304461172), ('"SET"', 1.2023771130527212), ('"RPL35"', 1.2000112687491644), ('"RPL13"', 1.1818455470828557), ('"TFF1"', 1.1081888465981982), ('"RPLP1"', 1.085824228127824), ('"RPS27"', 1.0852468414036733), ('"HSPB1"', 1.0825363004278141), ('"RPS14"', 1.0771059085798058), ('"RPS12"', 1.059035505175214), ('"H1-5"', 1.0111956580136596)]
17 genes have a standard deviation greater than 75.526 in normoxia samples
Also for normoxic RNA strands most of the genes that have a mean higher than 1 have also the standard deviation higher than 1.
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_samples_summary.transpose()
normoxia_summary_t = normoxia_samples_summary.transpose()
# Calculate the difference in means
diff_mean = abs(hypoxia_summary_t['mean'] - normoxia_summary_t['mean'])
# Sort genes by difference in mean in descending order
sorted_genes = diff_mean.sort_values(ascending=False)
# Get the list of genes
genes_list = sorted_genes.index.tolist()
genes_list
# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes.reset_index()
# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']
# Display the DataFrame
sorted_genes_df[:50]
| Gene | Difference | |
|---|---|---|
| 0 | "TFF1" | 2.542827 |
| 1 | "RPS19" | 2.404517 |
| 2 | "MALAT1" | 2.156846 |
| 3 | "RPL13" | 1.569094 |
| 4 | "RPL35" | 1.386297 |
| 5 | "RPS14" | 1.365172 |
| 6 | "MT-RNR2" | 1.281619 |
| 7 | "RPL12" | 1.054707 |
| 8 | "TMSB10" | 1.041653 |
| 9 | "RPLP1" | 0.979401 |
| 10 | "H4C3" | 0.900977 |
| 11 | "RPL34" | 0.837802 |
| 12 | "KRT19" | 0.836440 |
| 13 | "RPS27" | 0.818449 |
| 14 | "RPS5" | 0.813731 |
| 15 | "RPL35A" | 0.803219 |
| 16 | "LGALS1" | 0.801535 |
| 17 | "RPS12" | 0.773322 |
| 18 | "RPL15" | 0.747951 |
| 19 | "RPL39" | 0.731020 |
| 20 | "RPL36" | 0.685697 |
| 21 | "RPLP2" | 0.664837 |
| 22 | "HSPB1" | 0.650311 |
| 23 | "RPS15A" | 0.645334 |
| 24 | "RPS16" | 0.632702 |
| 25 | "S100A10" | 0.622019 |
| 26 | "SERF2" | 0.610647 |
| 27 | "GAPDH" | 0.588966 |
| 28 | "TPI1" | 0.586619 |
| 29 | "RPS15" | 0.534018 |
| 30 | "RPL30" | 0.528922 |
| 31 | "UQCRQ" | 0.525043 |
| 32 | "ELOB" | 0.512870 |
| 33 | "SET" | 0.507891 |
| 34 | "FTL" | 0.506052 |
| 35 | "ACTB" | 0.499447 |
| 36 | "ENO1" | 0.483760 |
| 37 | "PGK1" | 0.481790 |
| 38 | "KRT18" | 0.476327 |
| 39 | "MT-RNR1" | 0.470951 |
| 40 | "TMEM258" | 0.470342 |
| 41 | "RPL37A" | 0.469123 |
| 42 | "KRT8" | 0.466705 |
| 43 | "ROMO1" | 0.451809 |
| 44 | "RPS28" | 0.449777 |
| 45 | "HES1" | 0.428981 |
| 46 | "COX7A2" | 0.415697 |
| 47 | "SNRPD2" | 0.409989 |
| 48 | "PKM" | 0.404241 |
| 49 | "UBA52" | 0.381792 |
#we look at how many genes have a mean of expression greater than 1 and we print their names
#for hypoxia samples
hypoxia_samples_summary = hypoxia_samples.describe()
hypo_genes = hypoxia_samples_summary.loc['mean'][hypoxia_samples_summary.loc['mean'] > 1]
hypo_genes
"MALAT1" 2.367672 "TFF1" 3.293241 "H4C3" 2.260285 "TMSB10" 1.238426 "BCYRN1" 3.521130 "RPS5" 1.148638 "RPL12" 1.685798 "RPS12" 1.511378 "RPL39" 1.137316 "RPS16" 1.060643 "RPL35" 2.322385 "RPS19" 4.048201 "RPLP2" 1.184396 "RPL36" 1.111198 "RPL13" 2.500617 "RPS28" 1.011210 "RPL35A" 1.340881 "KRT19" 1.051788 "RPS14" 2.147069 "RPL34" 1.320368 "RPS15A" 1.084295 "KRT8" 1.460486 "RPL15" 1.145275 "RPS27" 1.644659 "KRT18" 1.028472 "HSPB1" 1.467548 "RPLP1" 1.800022 "SERF2" 1.102343 Name: mean, dtype: float64
#we do it also for normoxia samples
normoxia_samples_summary = normoxia_samples.describe()
normo_genes = normoxia_samples_summary.loc['mean'][normoxia_samples_summary.loc['mean'] > 1]
normo_genes
"MALAT1" 4.524518 "MT-RNR2" 1.379142 "H4C3" 1.359307 "BCYRN1" 3.188902 "RPS19" 1.643684 "SET" 1.054467 "ACTB" 1.486108 Name: mean, dtype: float64
We inspect the functions of the genes that have the highest difference in mean between the two samples:
The gene that has the highest difference is TFF1, we can see it as it's upregulated in hypoxia samples, this can highlight a relation as, while TFF1 is primarily known for its role in mucosal protection and repair in the gastrointestinal tract, it's plausible that its expression could be influenced by hypoxia in certain cellular contexts. For example, in tissues experiencing hypoxia due to compromised blood flow or ischemia, there may be alterations in the expression of genes involved in mucosal protection and repair, including TFF1.
We see that the gene MALAT1 is under-regulated in hypoxia samples, This can be a good indicator of hypoxia since studies have shown that MALAT1 expression can be modulated by HIFs under hypoxic conditions.
Moreover, the gene RPL13 is upregulated in hypoxia samples, also this feature can highlight some link as ribosomal proteins like RPL13 are primarily known for their role in protein synthesis and ribosome function, it's plausible that their expression could be influenced by hypoxia in certain cellular contexts, in fact some studies have indicated that the expression of ribosomal proteins, including RPL13, can be influenced by various cellular stressors and environmental conditions, including hypoxia.
In contrast, MT-RNR2 is downregulated in hypoxia, which is consistent with the known downregulation of mitochondrial activity in hypoxic conditions. in fact we have a mean of 0.097 in hypoxia and 1.38 in normoxia
Moreover the gene RPS19 is higly upregulated in the hypoxia samples, but in this case there is no evidence that it is correlated to the hipoxic condition as RPS19 expression is important for normal cellular function and its dysregulation can lead to diseases such as Diamond-Blackfan anemia, it's unlikely to be upregulated in hypoxic cells and may even be downregulated as part of the cellular response to low oxygen levels.
## Now with the std
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_samples_summary.transpose()
normoxia_summary_t = normoxia_samples_summary.transpose()
# Calculate the difference in means
diff_std = abs(hypoxia_summary_t['std'] - normoxia_summary_t['std'])
# Sort genes by difference in mean in descending order
sorted_genes_std = diff_std.sort_values(ascending=False)
# Get the list of genes
genes_list = sorted_genes_std.index.tolist()
genes_list
# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes_std.reset_index()
# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']
# Display the DataFrame
sorted_genes_df[:50]
| Gene | Difference | |
|---|---|---|
| 0 | "TFF1" | 1.963468 |
| 1 | "MT-RNR2" | 1.625194 |
| 2 | "H4C3" | 1.351073 |
| 3 | "RPS19" | 1.254369 |
| 4 | "BCYRN1" | 1.053588 |
| 5 | "MALAT1" | 0.938322 |
| 6 | "TMSB10" | 0.849760 |
| 7 | "MT-RNR1" | 0.769253 |
| 8 | "RPL13" | 0.731591 |
| 9 | "RPL35" | 0.712504 |
| 10 | "LGALS1" | 0.702786 |
| 11 | "RPS5" | 0.652149 |
| 12 | "RPL12" | 0.621375 |
| 13 | "RPS14" | 0.603336 |
| 14 | "KRT19" | 0.516626 |
| 15 | "RPL39" | 0.516618 |
| 16 | "RPS12" | 0.510186 |
| 17 | "HES1" | 0.507390 |
| 18 | "TPI1" | 0.492272 |
| 19 | "GAPDH" | 0.479824 |
| 20 | "RPL35A" | 0.469144 |
| 21 | "PGK1" | 0.456893 |
| 22 | "S100A10" | 0.454374 |
| 23 | "MT-ND4" | 0.442999 |
| 24 | "RPL36" | 0.440201 |
| 25 | "RPLP2" | 0.426425 |
| 26 | "TFF3" | 0.425001 |
| 27 | "RPS16" | 0.420057 |
| 28 | "RPS15A" | 0.419366 |
| 29 | "RPL34" | 0.419134 |
| 30 | "RPLP1" | 0.415865 |
| 31 | "RPL15" | 0.411792 |
| 32 | "RPS27" | 0.406568 |
| 33 | "SET" | 0.386002 |
| 34 | "RPL30" | 0.384034 |
| 35 | "NEAT1" | 0.376632 |
| 36 | "MT-CO3" | 0.374965 |
| 37 | "SOX4" | 0.369440 |
| 38 | "MT-ND3" | 0.362084 |
| 39 | "TOB1" | 0.360845 |
| 40 | "MT-CYB" | 0.360220 |
| 41 | "ROMO1" | 0.349800 |
| 42 | "RPS15" | 0.344840 |
| 43 | "ACTB" | 0.343785 |
| 44 | "S100A11" | 0.340564 |
| 45 | "DSCAM-AS1" | 0.338762 |
| 46 | "PARD6B" | 0.338344 |
| 47 | "TMEM258" | 0.336613 |
| 48 | "ATP1A1" | 0.331619 |
| 49 | "SNRPD2" | 0.329961 |
Even studying the std,the results are confirmed, genes in the first position are the same as before.
¶
3.1.4) UNSUPERVISD LEARNING APPLIED TO MCF7
We firstly decided to do a PCA of two components and then to perform a kmeans clustering.
The result was bad because, as can be seen from the first picture, the datapoints were not linearly separable.
The performance of the kmins in fact is just a division in two parts of the graph.
def extract_condition(name):
match = re.search(r'Normoxia|Hypoxia', name)
if match:
return match.group()
return 'unknown'
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(df_MCF7_filtered_T)
k = 2
kmeans = KMeans(n_clusters=k, init='k-means++',random_state=0, n_init=10)
clusters = kmeans.fit_predict(principalComponents)
plt.figure(figsize=(12, 6))
# Assign colors to conditions manually
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue', 'Unknown': 'green'}
conditions = df_MCF7_filtered_T.index.to_series().apply(extract_condition)
colors = conditions.map(color_map)
plt.scatter(principalComponents[:, 0], principalComponents[:, 1], c=colors)
plt.title('PCA of Cells Colored by Condition')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i],
label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')
plt.show()
# Visualization of PCA with K-means clusters, with a proper legend
plt.figure(figsize=(12, 6))
# Assign colors based on the K-means cluster result
# Here we assign 'green' to cluster 1 and 'yellow' to cluster 2
cluster_colors = ['green' if label == 0 else 'yellow' for label in clusters]
# Scatter plot with assigned colors
scatter = plt.scatter(principalComponents[:, 0], principalComponents[:, 1], c=cluster_colors)
# Create a legend for the colors
plt.title('PCA of Cells Colored by K-means Clusters')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
# Create a legend for the colors
legend_labels = {0: 'cluster 1', 1: 'cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')
plt.show()
Then we tried to apply a PCA of 3 components and then the kmins clustering.
Also in this case the datapoints were not linearly separable and thus the performance of the kmins was terrible.
#now i want to do a pca with 3 components
pca = PCA(n_components=3)
principalComponents3 = pca.fit_transform(df_MCF7_filtered_T)
clusters3 = kmeans.fit_predict(principalComponents3)
# Visualization of PCA in 3 dimensions, with a proper legend
pca = PCA(n_components=3)
principalComponents3 = pca.fit_transform(df_MCF7_filtered_T)
clusters3 = kmeans.fit_predict(principalComponents3)
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')
# Assign colors to conditions manually
colors = conditions.map(color_map)
# Scatter plot with assigned colors
scatter = ax.scatter(principalComponents3[:, 0], principalComponents3[:, 1], principalComponents3[:, 2], c=colors)
# Create a legend for the colors
ax.set_title('PCA of Cells Colored by Condition')
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i],
label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')
plt.show()
# Visualization of PCA with K-means clusters, with a proper legend
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')
# Assign colors based on the K-means cluster result
# Here we assign 'green' to cluster 1 and 'yellow' to cluster 0
cluster_colors3 = ['green' if label == 0 else 'yellow' for label in clusters3]
# Scatter plot with assigned colors
scatter = ax.scatter(principalComponents3[:, 0], principalComponents3[:, 1], principalComponents3[:, 2], c=cluster_colors3)
# Create a legend for the colors
ax.set_title('PCA of Cells Colored by K-means Clusters')
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
# Create a legend for the colors
legend_labels = {0: 'cluster 1', 1: 'cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')
plt.show()
As a last chance, we then tried to apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) to our data.
We did it as the t-SNE is a non-linear technique that is well-suited for capturing non-linear relationships in the data. It uses a non-linear optimization algorithm to map high-dimensional data points to a lower-dimensional space, making it more suitable for visualizing complex data with non-linear structures.
But also in this case the result was not good, both in the 2-dimensional and 3-dimensional case the datapoints were concentrated and not separable.
Thus the performance of k-mins was bad.
from sklearn.manifold import TSNE
# Dimensionality reduction with t-SNE
tsne3 = TSNE(n_components=3)
embedded_data3 = tsne3.fit_transform(df_MCF7_filtered_T)
# Apply k-means clustering to the t-SNE result
kmeans = KMeans(n_clusters=k, init='k-means++',random_state=0, n_init=10)
clustersT3 = kmeans.fit_predict(embedded_data3)
# Visualization of t-SNE in 3 dimensions, with a proper legend
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')
# Assign colors to conditions manually
colors = conditions.map(color_map)
# Scatter plot with assigned colors
scatter = ax.scatter(embedded_data3[:, 0], embedded_data3[:, 1], embedded_data3[:, 2], c=colors)
# Create a legend for the colors
ax.set_title('t-SNE of Cells Colored by Condition')
ax.set_xlabel('t-SNE Dimension 1')
ax.set_ylabel('t-SNE Dimension 2')
ax.set_zlabel('t-SNE Dimension 3')
# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i],
label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')
plt.show()
#plot the clusters
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')
# Assign colors based on the Spectral Clustering result
cluster_colorsT3 = ['green' if label == 0 else 'yellow' for label in clustersT3]
# Scatter plot with assigned colors
scatter = ax.scatter(embedded_data3[:, 0], embedded_data3[:, 1], embedded_data3[:, 2], c=cluster_colorsT3)
# Create a legend for the colors
ax.set_title('t-SNE of Cells Colored by Kmins Clustering')
ax.set_xlabel('t-SNE Dimension 1')
ax.set_ylabel('t-SNE Dimension 2')
ax.set_zlabel('t-SNE Dimension 3')
# Assuming that after any potential inversion, cluster 0 corresponds to Normoxia and cluster 1 to Hypoxia
legend_labels = {0: 'cluster 1', 1: 'cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')
plt.show()
from sklearn.manifold import TSNE
# Dimensionality reduction with t-SNE
tsne2 = TSNE(n_components=2)
embedded_data2 = tsne2.fit_transform(df_MCF7_filtered_T)
plt.figure(figsize=(12, 6))
#calculate the match rate with a k-means clustering
kmeansT2 = KMeans(n_clusters=2, init='k-means++',random_state=0, n_init=10)
clustersT2 = kmeansT2.fit_predict(embedded_data2)
# Assign colors to conditions manually
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue', 'Unknown': 'green'}
colors = conditions.map(color_map)
plt.scatter(embedded_data2[:, 0], embedded_data2[:, 1], c=colors)
plt.title('t-SNE of Cells Colored by Condition')
plt.xlabel('t-SNE Component 1')
plt.ylabel('t-SNE Component 2')
# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i],
label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')
plt.show()
#plot the clusters
plt.figure(figsize=(12, 6))
# Assign colors based on the K-means cluster result
cluster_colorsT2 = ['green' if label == 0 else 'yellow' for label in clustersT2]
# Scatter plot with assigned colors
scatter = plt.scatter(embedded_data2[:, 0], embedded_data2[:, 1], c=cluster_colorsT2)
# Create a legend for the colors
plt.title('t-SNE of Cells Colored by K-means Clusters')
plt.xlabel('t-SNE Component 1')
plt.ylabel('t-SNE Component 2')
# Assuming that after any potential inversion, cluster 0 corresponds to Normoxia and cluster 1 to Hypoxia
legend_labels = {0: 'cluster 1', 1: 'cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')
plt.show()
For the sake of curiosity, we finally tried to perform a "manual" PCA selecting 3 of the genes that where found as importantin the previous analysis.
But also the result we found was not separable and thus the performance of kmins was also in this case bad.
selected_genes = ['"TFF1"', '"MT-RNR2"', '"MALAT1"']
selected_genes_df = df_MCF7_filtered.loc[selected_genes]
# Transpose the DataFrame
selected_genes_df = selected_genes_df.T
# Apply PCA to the selected genes
pca = PCA(n_components=3)
selected_genes_transformed = pca.fit_transform(selected_genes_df)
# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue'}
colors = conditions.map(color_map)
# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=colors)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('PCA of Selected Genes (MT_RNR2, TFF1, MALAT1) Colored by Condition')
plt.show()
kmeans = KMeans(n_clusters=2, random_state=42)
clusters1 = kmeans.fit_predict(selected_genes_transformed)
# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypoxia': 'green', 'Normoxia': 'yellow'}
colors = conditions.map(color_map)
cluster_colorsH = ['yellow' if label == 0 else 'green' for label in clusters1]
# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=cluster_colorsH)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('K-means Clustering of Selected Genes (GAPDH, ALDOA, PGK1)')
plt.show()
/Users/teodorotoscano/opt/anaconda3/lib/python3.8/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning super()._check_params_vs_input(X, default_n_init=10)
¶
3.1.5) More unsupervised learning
X_MCF7_DROP = df_trans_MCF7_DROP.drop('TARGET', axis=1)
y_train_MCF7_DROP=df_trans_MCF7_DROP['TARGET']
scaler_MCF7_DROP = StandardScaler()
X_scaled_MCF7_DROP = scaler_MCF7_DROP.fit_transform(X_MCF7_DROP)
pca2_MCF7_DROP = PCA(n_components=2)
X_pca2_MCF7_DROP = pca2_MCF7_DROP.fit_transform(X_scaled_MCF7_DROP)
plt.figure(figsize=(12, 6))
plt.scatter(X_pca2_MCF7_DROP[:,0][y_train_MCF7_DROP==1], X_pca2_MCF7_DROP[:,1][y_train_MCF7_DROP==1], c='r')
plt.scatter(X_pca2_MCF7_DROP[:,0][y_train_MCF7_DROP==0], X_pca2_MCF7_DROP[:,1][y_train_MCF7_DROP==0], c='b')
plt.title("Clusterization PCA MCF7_DROP")
plt.show()
'''% of EXPLAINED VARIANCE'''
pca_MCF7_DROP = PCA().fit(X_scaled_MCF7_DROP)
explained_variance_ratio_MCF7_DROP = pca_MCF7_DROP.explained_variance_ratio_
cumulative_variance_MCF7_DROP = explained_variance_ratio_MCF7_DROP.cumsum()
plt.plot(range(1, len(cumulative_variance_MCF7_DROP) + 1), cumulative_variance_MCF7_DROP, marker='o')
plt.xlabel("Number of components")
plt.ylabel("Cumulative proportion of Variance Explained")
plt.title("Analysis of explained variance")
plt.show()
tsne_model_MCF7_DROP = TSNE(n_components=2) #, perplexity=10
X_tsne_MCF7_DROP = tsne_model_MCF7_DROP.fit_transform(X_scaled_MCF7_DROP)
# Visualization of results
plt.figure(figsize=(12, 6))
# plt.scatter(X_train_pca[:,0], X_train_pca[:,1], cmap='viridis')
plt.scatter(X_tsne_MCF7_DROP[:,0][y_train_MCF7_DROP==1], X_tsne_MCF7_DROP[:,1][y_train_MCF7_DROP==1], c='r')
plt.scatter(X_tsne_MCF7_DROP[:,0][y_train_MCF7_DROP==0], X_tsne_MCF7_DROP[:,1][y_train_MCF7_DROP==0], c='b')
plt.title("Clusterization TSNE ")
plt.show()
¶
3.2) HCC1806
¶
3.2.0) OPENING THE MAIN DATASET: HCC1806 DATASET PRODUCED USING THE DROPSEQ TECHNOLOGY
We start now opening the HCC1806 DropSeq filtered normalized dataset since we are interested in analyzing it.
# The dataset filtered is useful to apply statistical tools
df_HCC1806_filtered = pd.read_csv("DropSeq/HCC1806_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)
print("Dataframe dimensions:", np.shape(df_HCC1806_filtered))
print("First column: ", df_HCC1806_filtered.iloc[ : , :])
Dataframe dimensions: (3000, 14682)
First column: "AAAAAACCCGGC_Normoxia" "AAAACCGGATGC_Normoxia" \
"H1-5" 2 2
"MALAT1" 3 3
"MT-RNR2" 0 0
"ARVCF" 0 0
"BCYRN1" 0 1
... ... ...
"SCCPDH" 0 0
"NTAN1" 0 0
"CLIP2" 0 0
"DUSP23" 0 0
"ZNF682" 0 0
"AAAACGAGCTAG_Normoxia" "AAAACTTCCCCG_Normoxia" \
"H1-5" 5 1
"MALAT1" 2 3
"MT-RNR2" 0 0
"ARVCF" 0 0
"BCYRN1" 1 0
... ... ...
"SCCPDH" 0 0
"NTAN1" 0 0
"CLIP2" 0 0
"DUSP23" 0 0
"ZNF682" 0 0
"AAAAGCCTACCC_Normoxia" "AAACACAAATCT_Normoxia" \
"H1-5" 0 0
"MALAT1" 12 3
"MT-RNR2" 0 0
"ARVCF" 0 0
"BCYRN1" 0 1
... ... ...
"SCCPDH" 0 0
"NTAN1" 0 0
"CLIP2" 0 0
"DUSP23" 0 0
"ZNF682" 0 0
"AAACCAAGCCCA_Normoxia" "AAACCATGCACT_Normoxia" \
"H1-5" 0 0
"MALAT1" 1 2
"MT-RNR2" 0 0
"ARVCF" 0 0
"BCYRN1" 1 2
... ... ...
"SCCPDH" 0 0
"NTAN1" 0 0
"CLIP2" 0 0
"DUSP23" 0 0
"ZNF682" 0 0
"AAACCTCCGGCT_Normoxia" "AAACGCCGGTCC_Normoxia" ... \
"H1-5" 1 0 ...
"MALAT1" 0 0 ...
"MT-RNR2" 0 1 ...
"ARVCF" 0 0 ...
"BCYRN1" 0 3 ...
... ... ... ...
"SCCPDH" 0 0 ...
"NTAN1" 0 0 ...
"CLIP2" 0 0 ...
"DUSP23" 0 0 ...
"ZNF682" 0 0 ...
"TTTTCTGATGGT_Hypoxia" "TTTTGATTCAGA_Hypoxia" \
"H1-5" 0 1
"MALAT1" 3 1
"MT-RNR2" 1 2
"ARVCF" 0 0
"BCYRN1" 1 1
... ... ...
"SCCPDH" 0 0
"NTAN1" 0 0
"CLIP2" 0 0
"DUSP23" 0 0
"ZNF682" 0 0
"TTTTGCAACTGA_Hypoxia" "TTTTGCCGGGCC_Hypoxia" \
"H1-5" 0 2
"MALAT1" 1 1
"MT-RNR2" 2 2
"ARVCF" 0 0
"BCYRN1" 0 1
... ... ...
"SCCPDH" 0 0
"NTAN1" 0 0
"CLIP2" 0 0
"DUSP23" 0 0
"ZNF682" 0 0
"TTTTGTTAGCCT_Hypoxia" "TTTTTACCAATC_Hypoxia" \
"H1-5" 1 0
"MALAT1" 4 0
"MT-RNR2" 0 0
"ARVCF" 0 0
"BCYRN1" 1 0
... ... ...
"SCCPDH" 0 0
"NTAN1" 1 0
"CLIP2" 0 0
"DUSP23" 0 0
"ZNF682" 0 0
"TTTTTCCGTGCA_Hypoxia" "TTTTTGCCTGGG_Hypoxia" \
"H1-5" 0 0
"MALAT1" 4 1
"MT-RNR2" 1 0
"ARVCF" 0 0
"BCYRN1" 0 1
... ... ...
"SCCPDH" 0 0
"NTAN1" 0 0
"CLIP2" 0 0
"DUSP23" 0 0
"ZNF682" 0 0
"TTTTTGTAACAG_Hypoxia" "TTTTTTTGAATC_Hypoxia"
"H1-5" 3 1
"MALAT1" 3 6
"MT-RNR2" 1 0
"ARVCF" 0 0
"BCYRN1" 0 0
... ... ...
"SCCPDH" 0 0
"NTAN1" 0 0
"CLIP2" 0 0
"DUSP23" 0 0
"ZNF682" 0 0
[3000 rows x 14682 columns]
df_HCC1806_filtered.head()
| "AAAAAACCCGGC_Normoxia" | "AAAACCGGATGC_Normoxia" | "AAAACGAGCTAG_Normoxia" | "AAAACTTCCCCG_Normoxia" | "AAAAGCCTACCC_Normoxia" | "AAACACAAATCT_Normoxia" | "AAACCAAGCCCA_Normoxia" | "AAACCATGCACT_Normoxia" | "AAACCTCCGGCT_Normoxia" | "AAACGCCGGTCC_Normoxia" | ... | "TTTTCTGATGGT_Hypoxia" | "TTTTGATTCAGA_Hypoxia" | "TTTTGCAACTGA_Hypoxia" | "TTTTGCCGGGCC_Hypoxia" | "TTTTGTTAGCCT_Hypoxia" | "TTTTTACCAATC_Hypoxia" | "TTTTTCCGTGCA_Hypoxia" | "TTTTTGCCTGGG_Hypoxia" | "TTTTTGTAACAG_Hypoxia" | "TTTTTTTGAATC_Hypoxia" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "H1-5" | 2 | 2 | 5 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 3 | 1 |
| "MALAT1" | 3 | 3 | 2 | 3 | 12 | 3 | 1 | 2 | 0 | 0 | ... | 3 | 1 | 1 | 1 | 4 | 0 | 4 | 1 | 3 | 6 |
| "MT-RNR2" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | ... | 1 | 2 | 2 | 2 | 0 | 0 | 1 | 0 | 1 | 0 |
| "ARVCF" | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| "BCYRN1" | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 2 | 0 | 3 | ... | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 |
5 rows × 14682 columns
df_HCC1806_filtered.shape
(3000, 14682)
The MCF7 DropSeq dataset is made of 3000 rows representing specific genes and 14682 columns representing the RNA strands. Therefore each entry tells us how much of every gene is expressed in every RNA strand. Moreover, each RNA strand has in its name written the state in which it was under (normoxia / hypoxia).
¶
3.2.1) SPARSITY OF THE DATA
Since the data is already filtered and normalized, we can directy proceed on seeing how many zeros we have and then compute the percentage of sparcity.
# We proceed in the following way. We start representing the dataframe as a simple matrix to see how many zero we have
# then we compute the percentage of the sparsity on our dataset doing 1 - number_of_non_zero_genes / total_number of genes
df_HCC1806_filtered_Matrix = np.array(df_HCC1806_filtered)
print("Matrix useful for the sparsity check: \n", df_HCC1806_filtered_Matrix)
Matrix useful for the sparsity check: [[2 2 5 ... 0 3 1] [3 3 2 ... 1 3 6] [0 0 0 ... 0 1 0] ... [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0]]
Sparsity_matrix_HCC1806 = 1 - count_nonzero(df_HCC1806_filtered_Matrix)/ df_HCC1806_filtered_Matrix.size
Sparsity_matrix_HCC1806
0.9763756073196204
As we can see also for this dataset the percentage of sparcity is extremely high. This is not something unexpected as the RNA strands are small and contain only few genes.
This leads us also in this case to skip the outliers removal as the only genes that are expressed would in this case be detected as outliers and then we would lose all the informations.
¶
3.2.2) DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS
We transpose the dataset since having the genes in the colums and the RNA strands in the rows is more intresting under a statistical viewpoint.
#indeed looking at the transposed dataframe is more useful as genes are in the columns
df_HCC1806_filtered_T = df_HCC1806_filtered.T
df_HCC1806_filtered_summary2 = df_HCC1806_filtered_T.describe()
df_HCC1806_filtered_summary2
| "H1-5" | "MALAT1" | "MT-RNR2" | "ARVCF" | "BCYRN1" | "ATXN7L2" | "IGFBP3" | "H1-3" | "CTIF" | "RNF123" | ... | "BATF3" | "CDKN3" | "DLD" | "PMPCA" | "ZNF165" | "SCCPDH" | "NTAN1" | "CLIP2" | "DUSP23" | "ZNF682" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | ... | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 | 14682.000000 |
| mean | 0.709372 | 1.899333 | 0.861327 | 0.001294 | 1.038142 | 0.001498 | 0.371067 | 0.675862 | 0.000613 | 0.000613 | ... | 0.000477 | 0.045498 | 0.026563 | 0.010693 | 0.004155 | 0.009672 | 0.006471 | 0.002997 | 0.019003 | 0.000477 |
| std | 1.680187 | 2.790889 | 1.470822 | 0.047393 | 1.634813 | 0.052176 | 0.905313 | 1.292052 | 0.027366 | 0.027366 | ... | 0.024755 | 0.239997 | 0.189220 | 0.121667 | 0.073238 | 0.114546 | 0.090554 | 0.064917 | 0.156962 | 0.024755 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 1.000000 | 3.000000 | 1.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 58.000000 | 110.000000 | 23.000000 | 4.000000 | 33.000000 | 3.000000 | 14.000000 | 22.000000 | 2.000000 | 2.000000 | ... | 2.000000 | 4.000000 | 4.000000 | 5.000000 | 3.000000 | 3.000000 | 3.000000 | 4.000000 | 5.000000 | 2.000000 |
8 rows × 3000 columns
We can see that the mean of most genes is across zero. This was predictable as the sparcity analysis told us that the 97,6% of the entries is 0.
We then plot the 25/50/75% and we can clearly see that the non-zero genes are almost all located in the 75% quartile.
#we make a histogram of the mean of the expression of the genes
sns.displot(df_HCC1806_filtered_summary2.loc['mean'], bins=30, kde=True)
plt.show()
#we make a histogram of the standard deviation of the expression of the genes
sns.displot(df_HCC1806_filtered_summary2.loc['std'], bins=30, kde=True)
plt.show()
#we look only at the 25% quantile
sns.displot(df_HCC1806_filtered_summary2.loc['25%'], bins=30, kde=True)
plt.show()
df_HCC1806_filtered_summary2.loc['25%'].value_counts()
25% 0.0 2997 1.0 3 Name: count, dtype: int64
#we look only at the 50% quantile
sns.displot(df_HCC1806_filtered_summary2.loc['50%'], bins=30, kde=True)
plt.show()
df_HCC1806_filtered_summary2.loc['50%'].value_counts()
50% 0.0 2986 1.0 11 2.0 2 3.0 1 Name: count, dtype: int64
#we look only at the 75% quantile
sns.displot(df_HCC1806_filtered_summary2.loc['75%'], bins=30, kde=True)
plt.show()
df_HCC1806_filtered_summary2.loc['75%'].value_counts()
75% 0.0 2924 1.0 61 2.0 11 4.0 2 3.0 1 5.0 1 Name: count, dtype: int64
¶
3.2.3) LOOKING FOR MOST USEFUL AND EXPRESSED GENES ACROSS CELLS
Since the mean of most of the genes as seen before is near zero, we restrict our search to the genes that have at least a mean expression of 1.
Also for what regards the standard deviation we restrict our search to the genes that have a standard deviation higher 1.
We split the dataset into two parts containing normoxic RNA strands and hypoxic RNA strands.
#i want to split the dataset in 2 parts, one with RNA with hypoxia in the name and the other with RNA with normoxia in the name
hypoxia_samples = df_HCC1806_filtered.T[df_HCC1806_filtered.T.index.str.contains('Hypoxia')]
normoxia_samples = df_HCC1806_filtered.T[df_HCC1806_filtered.T.index.str.contains('Normoxia')]
hypoxia_samples.describe()
| "H1-5" | "MALAT1" | "MT-RNR2" | "ARVCF" | "BCYRN1" | "ATXN7L2" | "IGFBP3" | "H1-3" | "CTIF" | "RNF123" | ... | "BATF3" | "CDKN3" | "DLD" | "PMPCA" | "ZNF165" | "SCCPDH" | "NTAN1" | "CLIP2" | "DUSP23" | "ZNF682" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | ... | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 | 8899.000000 |
| mean | 0.659512 | 1.640634 | 0.805933 | 0.001910 | 0.660411 | 0.000562 | 0.558602 | 0.710305 | 0.000674 | 0.000562 | ... | 0.000337 | 0.044612 | 0.022587 | 0.008316 | 0.003596 | 0.009776 | 0.006967 | 0.001798 | 0.018654 | 0.000674 |
| std | 1.556327 | 2.488606 | 1.396132 | 0.058994 | 1.084628 | 0.035156 | 1.088374 | 1.354096 | 0.029977 | 0.028042 | ... | 0.018359 | 0.244813 | 0.178169 | 0.097959 | 0.073359 | 0.119068 | 0.096911 | 0.044941 | 0.164541 | 0.029977 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 1.000000 | 2.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 38.000000 | 32.000000 | 23.000000 | 4.000000 | 13.000000 | 3.000000 | 14.000000 | 22.000000 | 2.000000 | 2.000000 | ... | 1.000000 | 4.000000 | 3.000000 | 2.000000 | 3.000000 | 3.000000 | 3.000000 | 2.000000 | 5.000000 | 2.000000 |
8 rows × 3000 columns
normoxia_samples.describe()
| "H1-5" | "MALAT1" | "MT-RNR2" | "ARVCF" | "BCYRN1" | "ATXN7L2" | "IGFBP3" | "H1-3" | "CTIF" | "RNF123" | ... | "BATF3" | "CDKN3" | "DLD" | "PMPCA" | "ZNF165" | "SCCPDH" | "NTAN1" | "CLIP2" | "DUSP23" | "ZNF682" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 5783.000000 | 5783.000000 | 5783.000000 | 5783.000000 | 5783.000000 | 5783.00000 | 5783.000000 | 5783.000000 | 5783.000000 | 5783.000000 | ... | 5783.000000 | 5783.000000 | 5783.000000 | 5783.000000 | 5783.000000 | 5783.000000 | 5783.000000 | 5783.000000 | 5783.000000 | 5783.000000 |
| mean | 0.786097 | 2.297423 | 0.946568 | 0.000346 | 1.619402 | 0.00294 | 0.082483 | 0.622860 | 0.000519 | 0.000692 | ... | 0.000692 | 0.046861 | 0.032682 | 0.014352 | 0.005015 | 0.009511 | 0.005706 | 0.004842 | 0.019540 | 0.000173 |
| std | 1.852219 | 3.159882 | 1.575193 | 0.018595 | 2.101921 | 0.07076 | 0.347361 | 1.188442 | 0.022772 | 0.026293 | ... | 0.032206 | 0.232406 | 0.204930 | 0.150985 | 0.073050 | 0.107225 | 0.079791 | 0.087100 | 0.144537 | 0.013150 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 1.000000 | 3.000000 | 1.000000 | 0.000000 | 2.000000 | 0.00000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 58.000000 | 110.000000 | 23.000000 | 1.000000 | 33.000000 | 3.00000 | 8.000000 | 22.000000 | 1.000000 | 1.000000 | ... | 2.000000 | 4.000000 | 4.000000 | 5.000000 | 2.000000 | 3.000000 | 2.000000 | 4.000000 | 2.000000 | 1.000000 |
8 rows × 3000 columns
#we look at how many genes have a mean of expression greater than 1 and we print their names
#for hypoxia samples
hypoxia_samples_summary = hypoxia_samples.describe()
hypo_genes = hypoxia_samples_summary.loc['mean'][hypoxia_samples_summary.loc['mean'] > 1]
hypo_genes_sorted = sorted(hypo_genes.items(), key=lambda x: x[1], reverse=True)
print(hypo_genes_sorted)
print(f"{len(hypo_genes_sorted)} genes have a mean of expression greater than 1 in hypoxia samples")
## Now we apply the same with the standard deviation
hypo_genes_std = hypoxia_samples_summary.loc['std'][hypoxia_samples_summary.loc['std'] > 1]
hypo_genes_std_sorted = sorted(hypo_genes_std.items(), key=lambda x: x[1], reverse=True)
print(hypo_genes_std_sorted)
print(f"{len(hypo_genes_std_sorted)} genes have a standard deviation greater than 1 in hypoxia samples")
[('"H4C3"', 3.118889762894707), ('"FTL"', 3.0734914035284864), ('"RPS5"', 2.4254410607933474), ('"ENO1"', 1.7242386785032027), ('"MALAT1"', 1.6406337790763008), ('"HSP90B1"', 1.6295089335880435), ('"RPS28"', 1.5935498370603438), ('"RPL41"', 1.4645465782672211), ('"RPL8"', 1.213282391279919), ('"HSP90AA1"', 1.185975952354197), ('"HSP90AB1"', 1.1049556129902236)]
11 genes have a mean of expression greater than 1 in hypoxia samples
[('"H4C3"', 2.8476371578300013), ('"FTL"', 2.5232552155263117), ('"MALAT1"', 2.4886062420184993), ('"RPS5"', 2.137477715655018), ('"HSP90B1"', 2.0808422766117287), ('"ENO1"', 1.8570297304465508), ('"RPS28"', 1.6715214669450547), ('"H1-5"', 1.5563269417106602), ('"RPL41"', 1.531770551050613), ('"MT-RNR2"', 1.3961321004928406), ('"RPL8"', 1.372234955432059), ('"HSP90AA1"', 1.363856478982739), ('"H1-3"', 1.3540962502110523), ('"CALR"', 1.3255514472481256), ('"HSP90AB1"', 1.318641158199809), ('"RPL35"', 1.255768175970864), ('"RPLP2"', 1.253896852848974), ('"RPS2"', 1.1714264035549466), ('"RPLP1"', 1.1481861821292367), ('"NCL"', 1.1467903607169887), ('"RPS12"', 1.1333494220241271), ('"HSPA5"', 1.1223971895645422), ('"RPS19"', 1.0984475361923334), ('"IGFBP3"', 1.0883735997373036), ('"BCYRN1"', 1.0846278152986264), ('"HNRNPU"', 1.0695292597243782), ('"RPS27"', 1.0488082810291641), ('"LDHA"', 1.0389707223698534), ('"YBX1"', 1.038023909272488), ('"SERF2"', 1.0370512555557534), ('"EZR"', 1.0325425277623), ('"RPS15"', 1.0294956691922739), ('"RPS8"', 1.0158719090842991)]
33 genes have a standard deviation greater than 1 in hypoxia samples
We can see that in hypoxic RNA strands most of the genes that have a mean higher than 1 have also the standard deviation higher than 1, in fact, up to permutations of the position, the first 5 genes are the same.
normoxia_samples_summary = normoxia_samples.describe()
normo_genes = normoxia_samples_summary.loc['mean'][normoxia_samples_summary.loc['mean'] > 1]
normo_genes_sorted = sorted(normo_genes.items(), key=lambda x: x[1], reverse=True)
print(normo_genes_sorted)
print(f" {len(normo_genes_sorted)} genes have a mean of expression greater than 92.814 in normoxia samples")
normo_genes_std = normoxia_samples_summary.loc['std'][normoxia_samples_summary.loc['std'] > 1]
normo_genes_std_sorted = sorted(normo_genes_std.items(), key=lambda x: x[1], reverse=True)
print(normo_genes_std_sorted)
print(f"{len(normo_genes_std_sorted)} genes have a standard deviation greater than 75.526 in normoxia samples")
[('"H4C3"', 3.745979595365727), ('"FTL"', 3.056890887082829), ('"RPS5"', 2.897458066747363), ('"MALAT1"', 2.2974234826214768), ('"HSP90AA1"', 2.1028877745114993), ('"HSP90AB1"', 1.8661594328203355), ('"BCYRN1"', 1.6194016946221683), ('"NCL"', 1.426941034065364), ('"HSP90B1"', 1.3441120525678714), ('"RPL8"', 1.2953484350683035), ('"RPL35"', 1.2038734220992564), ('"ENO1"', 1.1971295175514438), ('"HSPD1"', 1.1625453916652257), ('"RPS28"', 1.0747017119142315), ('"AKR1C2"', 1.0050146982535015)]
15 genes have a mean of expression greater than 92.814 in normoxia samples
[('"H4C3"', 3.576264136244412), ('"MALAT1"', 3.1598821180514505), ('"FTL"', 2.8649494710788774), ('"RPS5"', 2.5193884163947264), ('"BCYRN1"', 2.1019214714967513), ('"H1-5"', 1.8522193169345953), ('"HSP90AB1"', 1.7330693785722528), ('"HSP90AA1"', 1.671157537583227), ('"MT-RNR2"', 1.5751930127971736), ('"HSP90B1"', 1.489570394829847), ('"ENO1"', 1.4844560903313344), ('"NCL"', 1.4798825401244873), ('"RPL35"', 1.3296653334142248), ('"RPL8"', 1.3152081767018833), ('"RPS28"', 1.308364441984559), ('"AKR1C2"', 1.2363998695618124), ('"HSPD1"', 1.2169680764817927), ('"CALR"', 1.2055489889868944), ('"HSPA5"', 1.189530811541049), ('"H1-3"', 1.1884415303903189), ('"HNRNPU"', 1.1503620069188694), ('"HNRNPA2B1"', 1.1283381343863612), ('"RPLP2"', 1.0840653425607143), ('"RPL41"', 1.0746106310381314), ('"RPS12"', 1.0547394319874752), ('"EZR"', 1.0358439770900387), ('"CALM2"', 1.0065949050864629)]
27 genes have a standard deviation greater than 75.526 in normoxia samples
Also for normoxic RNA strands most of the genes that have a mean higher than 1 have also the standard deviation higher than 1.
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_samples_summary.transpose()
normoxia_summary_t = normoxia_samples_summary.transpose()
# Calculate the difference in means
diff_mean = abs(hypoxia_summary_t['mean'] - normoxia_summary_t['mean'])
# Sort genes by difference in mean in descending order
sorted_genes = diff_mean.sort_values(ascending=False)
# Get the list of genes
genes_list = sorted_genes.index.tolist()
genes_list
# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes.reset_index()
# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']
# Display the DataFrame
sorted_genes_df[:50]
| Gene | Difference | |
|---|---|---|
| 0 | "BCYRN1" | 0.958990 |
| 1 | "HSP90AA1" | 0.916912 |
| 2 | "HSP90AB1" | 0.761204 |
| 3 | "MALAT1" | 0.656790 |
| 4 | "AKR1C2" | 0.654526 |
| 5 | "H4C3" | 0.627090 |
| 6 | "NCL" | 0.620671 |
| 7 | "HSPD1" | 0.602707 |
| 8 | "RPL41" | 0.599943 |
| 9 | "ENO1" | 0.527109 |
| 10 | "RPS28" | 0.518848 |
| 11 | "LDHA" | 0.490320 |
| 12 | "IGFBP3" | 0.476119 |
| 13 | "RPS5" | 0.472017 |
| 14 | "FGF23" | 0.444528 |
| 15 | "WDR43" | 0.443098 |
| 16 | "TMSB10" | 0.383676 |
| 17 | "GPM6A" | 0.380905 |
| 18 | "NDRG1" | 0.365190 |
| 19 | "AKR1C1" | 0.364441 |
| 20 | "CALM2" | 0.354971 |
| 21 | "S100A10" | 0.348586 |
| 22 | "EIF3J" | 0.316021 |
| 23 | "KRT19" | 0.286232 |
| 24 | "HSP90B1" | 0.285397 |
| 25 | "RPLP1" | 0.285041 |
| 26 | "RPS27" | 0.282417 |
| 27 | "HNRNPU" | 0.259843 |
| 28 | "S100A11" | 0.253016 |
| 29 | "SERF2" | 0.252565 |
| 30 | "RPL35" | 0.219830 |
| 31 | "ZC3H15" | 0.210185 |
| 32 | "YWHAZ" | 0.209874 |
| 33 | "NPM1P40" | 0.207242 |
| 34 | "P4HA1" | 0.204846 |
| 35 | "CAST" | 0.199762 |
| 36 | "CACNA1A" | 0.196696 |
| 37 | "RPL39" | 0.194336 |
| 38 | "S100A6" | 0.193717 |
| 39 | "B4GALT1" | 0.190929 |
| 40 | "RPS3" | 0.177411 |
| 41 | "NCALD" | 0.176731 |
| 42 | "PGK1" | 0.173551 |
| 43 | "PSMA7" | 0.170456 |
| 44 | "GAPDH" | 0.167722 |
| 45 | "EEF2" | 0.158171 |
| 46 | "RPL10" | 0.149857 |
| 47 | "HNRNPA2B1" | 0.147387 |
| 48 | "RPL27A" | 0.146624 |
| 49 | "TMSB4X" | 0.146578 |
#we do it also for normoxia samples
normoxia_samples_summary = normoxia_samples.describe()
normo_genes = normoxia_samples_summary.loc['mean'][normoxia_samples_summary.loc['mean'] > 1]
normo_genes
"MALAT1" 2.297423 "BCYRN1" 1.619402 "H4C3" 3.745980 "HSP90B1" 1.344112 "FTL" 3.056891 "ENO1" 1.197130 "AKR1C2" 1.005015 "RPS28" 1.074702 "RPS5" 2.897458 "NCL" 1.426941 "RPL35" 1.203873 "HSP90AB1" 1.866159 "HSPD1" 1.162545 "HSP90AA1" 2.102888 "RPL8" 1.295348 Name: mean, dtype: float64
#we look at the mean of the gene "BCYRN1" for hypoxia samples
hypoxia_samples_summary.loc['mean']['"BCYRN1"']
0.6604112821665356
#we look at the mean of the gene "RPL41" for normoxia samples
normoxia_samples_summary.loc['mean']['"RPL41"']
0.8646031471554556
We inspect the functions of the genes that seem to have the highest difference in mean between the two samples:
We see that as in the MCF7 cells also for this one we find a under-regulation of MALAT1 gene.
Also for what regards the gene BCYRN1, we see that it is very under-regulated in hypoxia samples, in this case there isn't direct evidence suggesting that BCYRN1 is under-regulated in cells in hypoxia. However, given that hypoxia can induce widespread changes in gene expression and cellular processes, it's plausible that BCYRN1 expression could be affected indirectly.
Moreover, the gene RPL41 is upregulated in hypoxia samples, this can be meaningful as in some studies, hypoxia has been shown to induce the upregulation of ribosomal protein genes, including RPL41, as part of the cellular response to low oxygen levels.
Finally, also for what regards the HSP90AA1 gene, we see that it is under-regulated in hypoxia samples, and it's a good discovery as there is also evidence suggesting that HSP90AA1 expression can be downregulated in hypoxia. Some studies have reported decreased levels of HSP90AA1 mRNA and protein under prolonged or severe hypoxic conditions.
## Now with the std
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_samples_summary.transpose()
normoxia_summary_t = normoxia_samples_summary.transpose()
# Calculate the difference in means
diff_std = abs(hypoxia_summary_t['std'] - normoxia_summary_t['std'])
# Sort genes by difference in mean in descending order
sorted_genes_std = diff_std.sort_values(ascending=False)
# Get the list of genes
genes_list = sorted_genes_std.index.tolist()
genes_list
# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes_std.reset_index()
# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']
# Display the DataFrame
sorted_genes_df[:50]
| Gene | Difference | |
|---|---|---|
| 0 | "BCYRN1" | 1.017294 |
| 1 | "IGFBP3" | 0.741012 |
| 2 | "H4C3" | 0.728627 |
| 3 | "MALAT1" | 0.671276 |
| 4 | "NDRG1" | 0.608544 |
| 5 | "HSP90B1" | 0.591272 |
| 6 | "FGF23" | 0.538811 |
| 7 | "AKR1C2" | 0.517095 |
| 8 | "LDHA" | 0.505089 |
| 9 | "GPM6A" | 0.496045 |
| 10 | "RPL41" | 0.457160 |
| 11 | "TMSB10" | 0.455659 |
| 12 | "HSP90AB1" | 0.414428 |
| 13 | "S100A10" | 0.399990 |
| 14 | "P4HA1" | 0.383122 |
| 15 | "RPS5" | 0.381911 |
| 16 | "AKR1C1" | 0.379500 |
| 17 | "ENO1" | 0.372574 |
| 18 | "RPS28" | 0.363157 |
| 19 | "RPS27" | 0.345836 |
| 20 | "FTL" | 0.341694 |
| 21 | "NCL" | 0.333092 |
| 22 | "BNIP3" | 0.329407 |
| 23 | "NPM1P40" | 0.325089 |
| 24 | "B4GALT1" | 0.324425 |
| 25 | "KRT19" | 0.320878 |
| 26 | "S100A11" | 0.318739 |
| 27 | "NCALD" | 0.314658 |
| 28 | "CACNA1A" | 0.312250 |
| 29 | "HSP90AA1" | 0.307301 |
| 30 | "RPLP1" | 0.307264 |
| 31 | "HSPD1" | 0.306199 |
| 32 | "H1-5" | 0.295892 |
| 33 | "RPL39" | 0.294623 |
| 34 | "PGK1" | 0.292165 |
| 35 | "MT-TQ" | 0.289243 |
| 36 | "WDR43" | 0.286854 |
| 37 | "BTBD9" | 0.283565 |
| 38 | "S100A6" | 0.278838 |
| 39 | "SERF2" | 0.277494 |
| 40 | "NEAT1" | 0.274250 |
| 41 | "EGLN3" | 0.272631 |
| 42 | "CAST" | 0.266825 |
| 43 | "H2AC12" | 0.260353 |
| 44 | "RPS3" | 0.254830 |
| 45 | "H4C5" | 0.252904 |
| 46 | "ZNF302" | 0.249627 |
| 47 | "PRRG3" | 0.241393 |
| 48 | "RPL10" | 0.236367 |
| 49 | "MT-TV" | 0.232770 |
Even studying the std,the results are confirmed, genes in the firsts position are similar to before.
¶
3.2.4) UNSUPERVISD LEARNING APPLIED TO MCF7
We firstly decided to do a PCA of two components and then to perform a kmeans clustering.
Also here the result was bad because, as can be seen from the first picture, the datapoints were not linearly separable.
The performance of the kmins in fact another time just a division in two parts of the graph.
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
import re
def extract_condition(name):
match = re.search(r'Normoxia|Hypoxia', name)
if match:
return match.group()
return 'unknown'
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(df_HCC1806_filtered_T)
k = 2
kmeans = KMeans(n_clusters=k, init='k-means++',random_state=0, n_init=10)
clusters = kmeans.fit_predict(principalComponents)
plt.figure(figsize=(12, 6))
# Assign colors to conditions manually
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue', 'Unknown': 'green'}
conditions = df_HCC1806_filtered_T.index.to_series().apply(extract_condition)
colors = conditions.map(color_map)
plt.scatter(principalComponents[:, 0], principalComponents[:, 1], c=colors)
plt.title('PCA of Cells Colored by Condition')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i],
label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')
plt.show()
# Visualization of PCA with K-means clusters, with a proper legend
plt.figure(figsize=(12, 6))
# Assign colors based on the K-means cluster result
cluster_colors = ['green' if label == 0 else 'yellow' for label in clusters]
# Scatter plot with assigned colors
scatter = plt.scatter(principalComponents[:, 0], principalComponents[:, 1], c=cluster_colors)
# Create a legend for the colors
plt.title('PCA of Cells Colored by K-means Clusters')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
legend_labels = {0: 'Cluster 1', 1: 'Cluester 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')
plt.show()
Then we tried to apply a PCA of 3 components and then the kmins clustering.
Also in this case the datapoints were not linearly separable and thus the performance of the kmins was terrible.
#now i want to do a pca with 3 components
pca = PCA(n_components=3)
principalComponents3 = pca.fit_transform(df_HCC1806_filtered_T)
clusters3 = kmeans.fit_predict(principalComponents3)
# Visualization of PCA in 3 dimensions, with a proper legend
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')
# Assign colors to conditions manually
colors = conditions.map(color_map)
# Scatter plot with assigned colors
scatter = ax.scatter(principalComponents3[:, 0], principalComponents3[:, 1], principalComponents3[:, 2], c=colors)
# Create a legend for the colors
ax.set_title('PCA of Cells Colored by Condition')
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i],
label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')
plt.show()
# Visualization of PCA with K-means clusters, with a proper legend
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')
# Assign colors based on the K-means cluster result
cluster_colors3 = ['green' if label == 0 else 'yellow' for label in clusters3]
# Scatter plot with assigned colors
scatter = ax.scatter(principalComponents3[:, 0], principalComponents3[:, 1], principalComponents3[:, 2], c=cluster_colors3)
# Create a legend for the colors
ax.set_title('PCA of Cells Colored by K-means Clusters')
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
# Assuming that after any potential inversion, cluster 0 corresponds to Normoxia and cluster 1 to Hypoxia
legend_labels = {0: 'Cluster 1', 1: 'Cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='division')
plt.show()
Also for this dataset we then tried to apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) to our data.
But also in this case the result was not good, both in the 2-dimensional and 3-dimensional case the datapoints were concentrated and not separable.
Thus the performance of k-mins was bad.
from sklearn.manifold import TSNE
# Dimensionality reduction with t-SNE
tsne3 = TSNE(n_components=3)
embedded_data3 = tsne3.fit_transform(df_HCC1806_filtered_T)
# Apply k-means clustering to the t-SNE result
k = 2
kmeans = KMeans(n_clusters=k, init='k-means++',random_state=0, n_init=10)
clustersT3 = kmeans.fit_predict(embedded_data3)
# Visualization of t-SNE in 3 dimensions, with a proper legend
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')
# Assign colors to conditions manually
colors = conditions.map(color_map)
# Scatter plot with assigned colors
scatter = ax.scatter(embedded_data3[:, 0], embedded_data3[:, 1], embedded_data3[:, 2], c=colors)
# Create a legend for the colors
ax.set_title('t-SNE of Cells Colored by Condition')
ax.set_xlabel('t-SNE Dimension 1')
ax.set_ylabel('t-SNE Dimension 2')
ax.set_zlabel('t-SNE Dimension 3')
# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i],
label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')
plt.show()
#plot the clusters
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')
# Assign colors based on the Spectral Clustering result
cluster_colorsT3 = ['green' if label == 0 else 'yellow' for label in clustersT3]
# Scatter plot with assigned colors
scatter = ax.scatter(embedded_data3[:, 0], embedded_data3[:, 1], embedded_data3[:, 2], c=cluster_colorsT3)
# Create a legend for the colors
ax.set_title('t-SNE of Cells Colored by Kmeans Clusters')
ax.set_xlabel('t-SNE Dimension 1')
ax.set_ylabel('t-SNE Dimension 2')
ax.set_zlabel('t-SNE Dimension 3')
legend_labels = {0: 'Cluster 1', 1: 'Cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')
plt.show()
from sklearn.manifold import TSNE
# Dimensionality reduction with t-SNE
tsne2 = TSNE(n_components=2)
embedded_data2 = tsne2.fit_transform(df_HCC1806_filtered_T)
plt.figure(figsize=(12, 6))
#apply kmeans clustering
k = 2
kmeans = KMeans(n_clusters=k, init='k-means++',random_state=0, n_init=10)
clustersT2 = kmeans.fit_predict(embedded_data2)
# Assign colors to conditions manually
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue', 'Unknown': 'green'}
colors = conditions.map(color_map)
plt.scatter(embedded_data2[:, 0], embedded_data2[:, 1], c=colors)
plt.title('t-SNE of Cells Colored by Condition')
plt.xlabel('t-SNE Component 1')
plt.ylabel('t-SNE Component 2')
# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i],
label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')
plt.show()
#plot the clusters
plt.figure(figsize=(12, 6))
# Assign colors based on the Spectral Clustering result
cluster_colors2 = ['green' if label == 0 else 'yellow' for label in clustersT2]
# Scatter plot with assigned colors
scatter = plt.scatter(embedded_data2[:, 0], embedded_data2[:, 1], c=cluster_colors2)
# Create a legend for the colors
plt.title('t-SNE of Cells Colored by Kmeans Clusters')
plt.xlabel('t-SNE Component 1')
plt.ylabel('t-SNE Component 2')
legend_labels = {0: 'cluster 1', 1: 'cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')
plt.show()
For the sake of curiosity, also in this case we finally tried to perform a "manual" PCA selecting 3 of the genes that where found as importantin the previous analysis.
But also the result we found was not separable and thus the performance of kmins was also in this case bad.
selected_genes = ['"BCYRN1"', '"HSP90AA1"', '"MALAT1"']
selected_genes_df = df_HCC1806_filtered.loc[selected_genes]
# Transpose the DataFrame
selected_genes_df = selected_genes_df.T
# Apply PCA to the selected genes
pca = PCA(n_components=3)
selected_genes_transformed = pca.fit_transform(selected_genes_df)
# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue'}
colors = conditions.map(color_map)
# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=colors)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('PCA of Selected Genes (MT_RNR2, TFF1, MALAT1) Colored by Condition')
plt.show()
kmeans = KMeans(n_clusters=2, random_state=42)
clusters1 = kmeans.fit_predict(selected_genes_transformed)
# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypoxia': 'green', 'Normoxia': 'yellow'}
colors = conditions.map(color_map)
cluster_colorsH = ['yellow' if label == 0 else 'green' for label in clusters1]
# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=cluster_colorsH)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('K-means Clustering of Selected Genes (GAPDH, ALDOA, PGK1)')
plt.show()
/Users/teodorotoscano/opt/anaconda3/lib/python3.8/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning super()._check_params_vs_input(X, default_n_init=10)
¶
3.2.5) More unsupervised learning
X_hcc_drop = df_trans_hcc_drop.drop('TARGET', axis=1)
y_train_hcc_drop=df_trans_hcc_drop['TARGET']
scaler_hcc_drop = StandardScaler()
X_scaled_hcc_drop = scaler_hcc_drop.fit_transform(X_hcc_drop)
pca2_hcc_drop = PCA(n_components=2)
X_pca2_hcc_drop = pca2_hcc_drop.fit_transform(X_scaled_hcc_drop)
plt.figure(figsize=(12, 6))
plt.scatter(X_pca2_hcc_drop[:,0][y_train_hcc_drop==1], X_pca2_hcc_drop[:,1][y_train_hcc_drop==1], c='r')
plt.scatter(X_pca2_hcc_drop[:,0][y_train_hcc_drop==0], X_pca2_hcc_drop[:,1][y_train_hcc_drop==0], c='b')
plt.title("Clusterization PCA hcc_drop")
plt.show()
'''% of EXPLAINED VARIANCE'''
pca_hcc_drop = PCA().fit(X_scaled_hcc_drop)
explained_variance_ratio_hcc_drop = pca_hcc_drop.explained_variance_ratio_
cumulative_variance_hcc_drop = explained_variance_ratio_hcc_drop.cumsum()
plt.plot(range(1, len(cumulative_variance_hcc_drop) + 1), cumulative_variance_hcc_drop, marker='o')
plt.xlabel("Number of components")
plt.ylabel("Cumulative proportion of Variance Explained")
plt.title("Analysis of explained variance")
plt.show()
tsne_model_hcc_drop = TSNE(n_components=2) #, perplexity=10
X_tsne_hcc_drop = tsne_model_hcc_drop.fit_transform(X_scaled_hcc_drop)
# Visualization of results
plt.figure(figsize=(12, 6))
# plt.scatter(X_train_pca[:,0], X_train_pca[:,1], cmap='viridis')
plt.scatter(X_tsne_hcc_drop[:,0][y_train_hcc_drop==1], X_tsne_hcc_drop[:,1][y_train_hcc_drop==1], c='r')
plt.scatter(X_tsne_hcc_drop[:,0][y_train_hcc_drop==0], X_tsne_hcc_drop[:,1][y_train_hcc_drop==0], c='b')
plt.title("Clusterization TSNE hcc_drop")
plt.show()
¶
- Classifiers
¶
4.0) Definitions
¶
4.0.1) Definition of Neural Networks
We defined four different neural networks models based on the dataset we used as input to train them.
¶
4.0.1.1) Definition of the neural network on single dataset (dropseq)
This neural network called "NeuralNetworkDropSeq" is the one we used with both the datasets DropSeq MCF7 and HCC1806 separately. The neural network has the following structure:
- a 'Flatten' layer to transform the input data into a one-dimensional array;
- a series of 'Linear' layers that progressively reduce in size: the first layer takes 3000 input features (from the MCF7 or HCC1806 data) and connects to 512 neurons, followed by reductions from 512 to 256, 256 to 128, and finally 128 to 64 neurons;
- 'LeakyReLU' activation is used after each Linear layer to introduce non-linearity;
- 'Dropout' layers are inserted after the first, second, and third 'LeakyReLU' activations to prevent overfitting by randomly setting 40% of the inputs to zero after the first and second layers, and 30% after the third layer;
- a 'BatchNorm1d' layer follows the second 'LeakyReLU' to normalize the activations from the second Linear layer, also aiding in reducing overfitting; The final output is through a single neuron that predicts a binary value (1 or 0) representing the state of the cell (normoxia or hypoxia)
class NeuralNetworkDropSeq(nn.Module):
def __init__(self):
super(NeuralNetworkDropSeq, self).__init__()
self.model = nn.Sequential(
nn.Flatten(),
nn.Linear(3000, 512), # Increased layer size
nn.LeakyReLU(),
nn.Dropout(0.4),
nn.Linear(512, 256), # Increased layer size
nn.LeakyReLU(),
nn.BatchNorm1d(256),
nn.Dropout(0.4),
nn.Linear(256, 128), # Increased layer size
nn.LeakyReLU(),
nn.Dropout(0.3),
nn.Linear(128, 64), # Kept the original layer
nn.LeakyReLU(),
nn.Linear(64, 1) # Output layer
)
def forward(self, x):
return self.model(x)
¶
4.0.1.2) Definition on merged dropseq
The following neural network called "NeuralNetworkDropSeqMerged" is the one we used with the merged datasets MCF7 and HCC1806. It has the same structure discussed before with the difference of the first linear layer which contains 5166 neurons (the reason is because the merged dataset has 5166 features).
class NeuralNetworkDropSeqMerged(nn.Module):
def __init__(self):
super(NeuralNetworkDropSeqMerged, self).__init__()
self.model = nn.Sequential(
nn.Flatten(),
nn.Linear(5166, 512), # Increased layer size
nn.LeakyReLU(),
nn.Dropout(0.4),
nn.Linear(512, 256), # Increased layer size
nn.LeakyReLU(),
nn.BatchNorm1d(256),
nn.Dropout(0.4),
nn.Linear(256, 128), # Increased layer size
nn.LeakyReLU(),
nn.Dropout(0.3),
nn.Linear(128, 64), # Kept the original layer
nn.LeakyReLU(),
nn.Linear(64, 1) # Output layer
)
def forward(self, x):
return self.model(x)
¶
4.0.1.3) Definition on general (all 4 dataset merged)
Finally, the last neural network we built, called "GP1" (general predictor), is the one we used with the general dataset (with features from both smartest and dropseq) and again has the same structure apart in the first layer where we have 8645 neurons.
class GP1(nn.Module):
def __init__(self):
super(GP1, self).__init__()
self.model = nn.Sequential(
nn.Flatten(),
nn.Linear(8645, 512), # Increased layer size
nn.LeakyReLU(),
nn.Dropout(0.4),
nn.Linear(512, 256), # Increased layer size
nn.LeakyReLU(),
nn.BatchNorm1d(256),
nn.Dropout(0.4),
nn.Linear(256, 128), # Increased layer size
nn.LeakyReLU(),
nn.Dropout(0.3),
nn.Linear(128, 64), # Kept the original layer
nn.LeakyReLU(),
nn.Linear(64, 1) # Output layer
)
def forward(self, x):
return self.model(x)
¶
4.0.2) Definition of KNN
def train_and_evaluate_knn(X_train, X_test, y_train, y_test):
#KNN model creation
neigh = KNeighborsClassifier()
#define CV strategy
cv = StratifiedKFold(4)
#we are going to optimize only one hyperparameter
param_grid = {'n_neighbors': [2,3,4]}
# lets train and optimize model
grid_search = GridSearchCV(neigh, param_grid, cv=cv)
grid_search.fit(X_train, y_train)
#get the best one
final_model = grid_search.best_estimator_
y_pred = final_model.predict(X_test)
print("Best hyperparameters: ", grid_search.best_params_)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("F1:", f1_score(y_test, y_pred))
print("Classification Report:")
print(classification_report(y_test, y_pred))
return final_model
¶
4.0.3) Definition Logistic Regression
def train_and_evaluate_logreg(X_train, X_test, y_train, y_test):
#log reg model creation
logreg = LogisticRegression()
#define CV strategy
cv = StratifiedKFold(4)
#we are going to optimize only one hyperparameter
param_grid = {'C': [0.0001,0.002,0.005,0.007,0.01,0.01,0.03,0.1,0.5],'penalty': ['l2']}
# lets train and optimize model
grid_search = GridSearchCV(logreg, param_grid, cv=cv)
grid_search.fit(X_train, y_train)
#get the best one
final_model = grid_search.best_estimator_
y_pred = final_model.predict(X_test)
print("Best hyperparameters: ", grid_search.best_params_)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("F1:", f1_score(y_test, y_pred))
print("Classification Report:")
print(classification_report(y_test, y_pred))
return final_model
¶
4.0.4) Definition of CatBoost
def train_and_evaluate_catboost(X_train, X_test, y_train, y_test):
#catboost model creation
catboost = CatBoostClassifier()
#define CV strategy
cv = StratifiedKFold(4)
#we are going to optimize only one hyperparameter
param_grid = {
# 'n_estimators': [200,500,750],
# 'l2_leaf_reg': [0,3,10],
# 'learning_rate' : [0.01,0.03,0.05],
'logging_level': ['Silent']
}
# lets train and optimize model
grid_search = GridSearchCV(catboost, param_grid, cv=cv)
grid_search.fit(X_train, y_train)
#get the best one
final_model = grid_search.best_estimator_
y_pred = final_model.predict(X_test)
print("Best hyperparameters: ", grid_search.best_params_)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("F1:", f1_score(y_test, y_pred))
print("Classification Report:")
print(classification_report(y_test, y_pred))
return final_model
def train_and_evaluate_catboost2(X_train, X_test, y_train, y_test):
#catboost model creation
catboost = CatBoostClassifier(logging_level = 'Silent')
catboost.fit(X_train,y_train)
# get features importance
importance=catboost.get_feature_importance()
names=X_train.columns
feature_importance = np.array(importance)
feature_names = np.array(names)
#Create a DataFrame using a Dictionary
data={'feature_names':feature_names,'feature_importance':feature_importance}
fi_df = pd.DataFrame(data)
#Sort the DataFrame in order decreasing feature importance
fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)
fi_df['acc_sum'] = fi_df.feature_importance.cumsum()
# get features provided 99% of information for model
col_cut=list(fi_df[fi_df.acc_sum<=99].feature_names.values)
print(len(col_cut))
catboost = CatBoostClassifier()
#define CV strategy
cv = StratifiedKFold(4)
#we are going to optimize only one hyperparameter
param_grid = {
'n_estimators': [500,1000,1500],
'l2_leaf_reg': [3,6,10],
# 'learning_rate' : [0.01,0.03,0.05],
'logging_level': ['Silent']
}
# lets train and optimize model
grid_search = GridSearchCV(catboost, param_grid, cv=cv)
grid_search.fit(X_train[col_cut], y_train)
#get the best one
final_model = grid_search.best_estimator_
y_pred = final_model.predict(X_test[col_cut])
print("Best hyperparameters: ", grid_search.best_params_)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("F1:", f1_score(y_test, y_pred))
print("Classification Report:")
print(classification_report(y_test, y_pred))
return final_model,col_cut
¶
4.0.5) Data transformation
def prepare_data(df):
X = df.drop('TARGET', axis=1)
y = df['TARGET']
# train test samples
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# scaler applying
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
return X_train_scaled, X_test_scaled, y_train, y_test,scaler
def prepare_data_pca(df):
X = df.drop('TARGET', axis=1)
y = df['TARGET']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# scaler applying
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# pca model, which provides at least 80% of variance
pca = PCA(0.8)
X_train_reduced = pca.fit_transform(X_train_scaled)
X_test_reduced = pca.transform(X_test_scaled)
return X_train_reduced, X_test_reduced, y_train, y_test,scaler,pca
¶
4.1) PREDICTORS SMARTSEQ MCF7
We first start by loading the dataset and then we add to it a 'target' column which contains 1 if the cell is grown in hypoxic conditions and 0 if not.
After that we divide it in the X dataset that contains the features and Y that contains the targets.
Finally, we split it into training and testing data, with a balance of 70% training and 30% testing.
# load the SmartSeq MCF7 dataset
df_SS_MCF7_norm_filt = pd.read_csv("SmartSeq\MCF7_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)
df_SS_MCF7_norm_filtT = df_SS_MCF7_norm_filt.T
# Add the target column
df_SS_MCF7_norm_filtT['target'] = np.where(df_SS_MCF7_norm_filtT.index.str.contains('Hypo'), 1, 0)
#i want to split the data into training and testing data, with a balance of 70% training and 30% testing
X_df_SS_MCF7= df_SS_MCF7_norm_filtT.drop('target', axis=1)
y_df_SS_MCF7 = df_SS_MCF7_norm_filtT['target']
X_train_df_SS_MCF7, X_test_df_SS_MCF7, y_train_df_SS_MCF7, y_test_df_SS_MCF7 = train_test_split(X_df_SS_MCF7, y_df_SS_MCF7, test_size=0.3, random_state=42)
df_basic_SS_MCF=pd.read_csv("SmartSeq\MCF7_SmartS_Filtered_Normalised_3000_Data_train.txt", delimiter="\ ", engine='python',index_col=0)
df_trans_SS_MCF=df_basic_SS_MCF.T
df_trans_SS_MCF['TARGET'] = df_trans_SS_MCF.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_SS_MCF.loc[df_trans_SS_MCF.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_SS_MCF.loc[df_trans_SS_MCF.index.str.contains('Hypo'), 'TARGET'] = 1
X_train_SS_MCF, X_test_SS_MCF, y_train_SS_MCF, y_test_SS_MCF,scaler_SS_MCF = prepare_data(df_trans_SS_MCF)
¶
4.1.1) SVM
First predictor we use are SVM. In particular, to find the best estimator we use a parametric grid for GridSearchCV function based on the kernel of the SVM (either linear or RBF). We also perform a 2 fold cross validation.
# build SVM on SmartSeq MCF7
# Define the SVM classifier
svm = SVC(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'kernel': ['linear', 'rbf']}
grid_search_svm = GridSearchCV(svm, param_grid, cv=2)
# Perform the cross validation
scores_svm = cross_val_score(grid_search_svm, X_train_df_SS_MCF7, y_train_df_SS_MCF7, cv=2)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of 2 fold cross validation: ", np.mean(scores_svm))
# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search_svm.fit(X_train_df_SS_MCF7, y_train_df_SS_MCF7)
print("Best hyperparameters: ", grid_search_svm.best_params_)
print("Best accuracy: ", grid_search_svm.best_score_)
best_svm_SS_MCF7 = grid_search_svm.best_estimator_
print("Best model: ", best_svm_SS_MCF7)
# Test the model on the test dataset
print("Test accuracy: ", grid_search_svm.best_estimator_.score(X_test_df_SS_MCF7, y_test_df_SS_MCF7))
Mean accuracy of 2 fold cross validation: 0.9942528735632183
Best hyperparameters: {'kernel': 'rbf'}
Best accuracy: 0.9942528735632183
Best model: SVC(random_state=42)
Test accuracy: 0.9866666666666667
¶
4.1.2) Random Forest
Here we build the random forest classifier for the SmartSeq MCF7 dataset.
To find the best parameters, we decided to define a parameter grid and perform a grid search.
To handle overfitting and to be sure that the parameters are surely the best over the other alternstives, we decided to perform a nested cross validation.
The benefits of doing it is that Nested cross-validation with LOOCV provides an unbiased estimate of the model's performance because each sample in the dataset serves as both training and test data at some point and moreover it ensures that hyperparameter tuning is conducted independently for each training/test split, reducing the risk of overfitting to the validation set.
After that we fit the best model found by the grid search with the training dataset and we print various important information.
As we can see from the output, in this case we have a 100% accuracy and the list of the most important features provide us a good ranking of genes that the model used mostly to discriminate about the hypoxic and normoxic conditions.
#i build the random forest classifier for the SmartSeq MCF7 dataset
# Define the random forest classifier
rf = RandomForestClassifier(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [10, 20, 50], 'max_depth': [10, 20, 30]}
grid_search = GridSearchCV(rf, param_grid, cv=5)
# Define the leave one out cross validation
loo = LeaveOneOut()
# Perform the nested cross validation
scores = cross_val_score(grid_search, X_train_df_SS_MCF7, y_train_df_SS_MCF7, cv=loo)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))
# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_SS_MCF7, y_train_df_SS_MCF7)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)
#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_SS_MCF7, y_test_df_SS_MCF7))
# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_SS_MCF7.shape[1]):
print("%d. feature %s (%f)" % (f + 1, X_train_df_SS_MCF7.columns[indices[f]], importances[indices[f]]))
best_rf_SS_MCF7 = grid_search.best_estimator_
Mean accuracy of nested cross validation: 0.9885714285714285
Best hyperparameters: {'max_depth': 10, 'n_estimators': 20}
Best accuracy: 0.9942857142857143
Best model: RandomForestClassifier(max_depth=10, n_estimators=20, random_state=42)
Test accuracy: 1.0
Feature ranking:
1. feature "PSME2" (0.084883)
2. feature "ERO1A" (0.082939)
3. feature "ARPC1B" (0.078651)
4. feature "TUBG1" (0.074686)
5. feature "BNIP3" (0.047712)
6. feature "P4HA1" (0.044656)
7. feature "CXXC5" (0.042232)
8. feature "GPI" (0.041295)
9. feature "PFKP" (0.039222)
10. feature "KPNA2" (0.034581)
11. feature "KLF3" (0.030159)
12. feature "DOLK" (0.027947)
13. feature "SRM" (0.027246)
14. feature "FANCG" (0.024916)
15. feature "ZNF395" (0.023657)
16. feature "C1QTNF6" (0.020101)
17. feature "UBC" (0.015791)
18. feature "FTH1" (0.013772)
19. feature "CRABP2" (0.013356)
20. feature "CRELD2" (0.011669)
21. feature "EGLN3" (0.010181)
22. feature "TLE1" (0.007734)
23. feature "MT-ATP6" (0.007637)
24. feature "SLC9A3R1-AS1" (0.007464)
25. feature "NECTIN2" (0.007417)
26. feature "DYNC2I2" (0.007275)
27. feature "MYO1E" (0.007012)
28. feature "GATA3-AS1" (0.006491)
29. feature "LDHA" (0.006099)
30. feature "NR4A1" (0.005884)
31. feature "SKIL" (0.005646)
32. feature "HILPDA" (0.005478)
33. feature "NDRG1" (0.005448)
34. feature "DCXR" (0.005397)
35. feature "ABAT" (0.005395)
36. feature "DSCAM-AS1" (0.005382)
37. feature "SYTL5" (0.005376)
38. feature "PRMT6" (0.005193)
39. feature "OBSL1" (0.005032)
40. feature "BTG1" (0.004431)
41. feature "TMEM164" (0.004196)
42. feature "DDX5" (0.004188)
43. feature "HK2" (0.004176)
44. feature "SLC3A2" (0.003820)
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2585. feature "EPHA1-AS1" (0.000000)
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2587. feature "CERS4" (0.000000)
2588. feature "COL13A1" (0.000000)
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2590. feature "TM7SF2" (0.000000)
2591. feature "FBXO27" (0.000000)
2592. feature "KLHDC8B" (0.000000)
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2597. feature "CYP26A1" (0.000000)
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2599. feature "COL6A3" (0.000000)
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2604. feature "FDPS" (0.000000)
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2606. feature "LYNX1-SLURP2" (0.000000)
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2608. feature "ZNF177" (0.000000)
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2627. feature "INSYN1" (0.000000)
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2641. feature "SOX8" (0.000000)
2642. feature "NFE2L2" (0.000000)
2643. feature "SFMBT2" (0.000000)
2644. feature "GPR161" (0.000000)
2645. feature "FAM47E-STBD1" (0.000000)
2646. feature "TACR1" (0.000000)
2647. feature "GPM6A" (0.000000)
2648. feature "CYGB" (0.000000)
2649. feature "UGT3A2" (0.000000)
2650. feature "UNC5CL" (0.000000)
2651. feature "SPATA13" (0.000000)
2652. feature "LINC01629" (0.000000)
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2654. feature "FAM102B" (0.000000)
2655. feature "MAOA" (0.000000)
2656. feature "WWC2" (0.000000)
2657. feature "NR1D1" (0.000000)
2658. feature "USP2" (0.000000)
2659. feature "PTGER3" (0.000000)
2660. feature "BACH2" (0.000000)
2661. feature "COL16A1" (0.000000)
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2664. feature "MYZAP" (0.000000)
2665. feature "CD74" (0.000000)
2666. feature "ADGRB1" (0.000000)
2667. feature "AKAP3" (0.000000)
2668. feature "SLC47A1" (0.000000)
2669. feature "ZDHHC11B" (0.000000)
2670. feature "CHRNA10" (0.000000)
2671. feature "EXTL1" (0.000000)
2672. feature "TMEM158" (0.000000)
2673. feature "COL11A2" (0.000000)
2674. feature "PAOX" (0.000000)
2675. feature "PCDHGB9P" (0.000000)
2676. feature "SCARF2" (0.000000)
2677. feature "LMNA" (0.000000)
2678. feature "GALNT12" (0.000000)
2679. feature "MECOM" (0.000000)
2680. feature "MYOZ1" (0.000000)
2681. feature "EFHD1" (0.000000)
2682. feature "HIPK4" (0.000000)
2683. feature "LINC02288" (0.000000)
2684. feature "PAM" (0.000000)
2685. feature "ST6GALNAC4" (0.000000)
2686. feature "CKS2" (0.000000)
2687. feature "SLC34A1" (0.000000)
2688. feature "TRAIP" (0.000000)
2689. feature "KCTD14" (0.000000)
2690. feature "NUP188" (0.000000)
2691. feature "CARD10" (0.000000)
2692. feature "GPX3" (0.000000)
2693. feature "ZSWIM3" (0.000000)
2694. feature "KIAA0319" (0.000000)
2695. feature "SPRY1" (0.000000)
2696. feature "CAPN10-DT" (0.000000)
2697. feature "TEX19" (0.000000)
2698. feature "GHDC" (0.000000)
2699. feature "COL24A1" (0.000000)
2700. feature "GRIK2" (0.000000)
2701. feature "CCDC63" (0.000000)
2702. feature "NAPSA" (0.000000)
2703. feature "PASK" (0.000000)
2704. feature "CLCN4" (0.000000)
2705. feature "KRT223P" (0.000000)
2706. feature "EDA" (0.000000)
2707. feature "BFSP2" (0.000000)
2708. feature "COL6A4P1" (0.000000)
2709. feature "ZNF589" (0.000000)
2710. feature "PSG4" (0.000000)
2711. feature "OXER1" (0.000000)
2712. feature "IFNLR1" (0.000000)
2713. feature "KIF1A" (0.000000)
2714. feature "BCAN-AS1" (0.000000)
2715. feature "LRRC2" (0.000000)
2716. feature "CA8" (0.000000)
2717. feature "SNRNP25" (0.000000)
2718. feature "FZR1" (0.000000)
2719. feature "DGAT2" (0.000000)
2720. feature "STC1" (0.000000)
2721. feature "FRMD4B" (0.000000)
2722. feature "SGSM2" (0.000000)
2723. feature "FAM234B" (0.000000)
2724. feature "PABPC4L" (0.000000)
2725. feature "FRK" (0.000000)
2726. feature "LRIG3" (0.000000)
2727. feature "LINC00963" (0.000000)
2728. feature "MYO15A" (0.000000)
2729. feature "ZNF304" (0.000000)
2730. feature "NTSR2" (0.000000)
2731. feature "CKAP5" (0.000000)
2732. feature "LINC01869" (0.000000)
2733. feature "ZBTB32" (0.000000)
2734. feature "KCNQ1" (0.000000)
2735. feature "LINC00242" (0.000000)
2736. feature "SLC45A1" (0.000000)
2737. feature "ABCD1" (0.000000)
2738. feature "COL7A1" (0.000000)
2739. feature "S100A11" (0.000000)
2740. feature "ZNF423" (0.000000)
2741. feature "TMIE" (0.000000)
2742. feature "TPPP" (0.000000)
2743. feature "SLC12A2" (0.000000)
2744. feature "SCART1" (0.000000)
2745. feature "NTNG1" (0.000000)
2746. feature "HEY2" (0.000000)
2747. feature "NOL6" (0.000000)
2748. feature "CDC42EP1" (0.000000)
2749. feature "LINC01426" (0.000000)
2750. feature "SLC4A9" (0.000000)
2751. feature "ANKRD2" (0.000000)
2752. feature "ASPHD2" (0.000000)
2753. feature "TMSB4X" (0.000000)
2754. feature "CDC25C" (0.000000)
2755. feature "KHK" (0.000000)
2756. feature "IL17C" (0.000000)
2757. feature "RAB37" (0.000000)
2758. feature "LAMA3" (0.000000)
2759. feature "FBLN7" (0.000000)
2760. feature "ANKRD62P1" (0.000000)
2761. feature "FBXO32" (0.000000)
2762. feature "GNMT" (0.000000)
2763. feature "ZFYVE1" (0.000000)
2764. feature "ADPRH" (0.000000)
2765. feature "NRARP" (0.000000)
2766. feature "OR2AF1P" (0.000000)
2767. feature "LDHD" (0.000000)
2768. feature "TRIM54" (0.000000)
2769. feature "MMP16" (0.000000)
2770. feature "CHKA-DT" (0.000000)
2771. feature "IGFBPL1" (0.000000)
2772. feature "PEX5" (0.000000)
2773. feature "CST8" (0.000000)
2774. feature "KRTAP5-AS1" (0.000000)
2775. feature "GAD1" (0.000000)
2776. feature "FAM3B" (0.000000)
2777. feature "MAP3K1" (0.000000)
2778. feature "NQO1" (0.000000)
2779. feature "HLA-B" (0.000000)
2780. feature "ADGRF2" (0.000000)
2781. feature "IGSF8" (0.000000)
2782. feature "RGS3" (0.000000)
2783. feature "MST1" (0.000000)
2784. feature "RPS6KA2" (0.000000)
2785. feature "FAM102A" (0.000000)
2786. feature "ADCY6" (0.000000)
2787. feature "SYTL4" (0.000000)
2788. feature "ACRV1" (0.000000)
2789. feature "SYNPO" (0.000000)
2790. feature "HTR1E" (0.000000)
2791. feature "HSD17B14" (0.000000)
2792. feature "CSF1R" (0.000000)
2793. feature "GPR179" (0.000000)
2794. feature "FAIM2" (0.000000)
2795. feature "PIEZO2" (0.000000)
2796. feature "RFC2" (0.000000)
2797. feature "VPS18" (0.000000)
2798. feature "RAB7B" (0.000000)
2799. feature "DOK6" (0.000000)
2800. feature "HACD4" (0.000000)
2801. feature "MDGA2" (0.000000)
2802. feature "TRIM47" (0.000000)
2803. feature "SOAT2" (0.000000)
2804. feature "SYTL3" (0.000000)
2805. feature "AGTR1" (0.000000)
2806. feature "TTC9" (0.000000)
2807. feature "FGD3" (0.000000)
2808. feature "SIGMAR1" (0.000000)
2809. feature "EPS8L3" (0.000000)
2810. feature "BACE2" (0.000000)
2811. feature "OTUD7A" (0.000000)
2812. feature "TRBV30" (0.000000)
2813. feature "PLEKHB1" (0.000000)
2814. feature "PPEF1" (0.000000)
2815. feature "PDE10A" (0.000000)
2816. feature "RNF19B" (0.000000)
2817. feature "LRRK1" (0.000000)
2818. feature "FGR" (0.000000)
2819. feature "ERN1" (0.000000)
2820. feature "PIDD1" (0.000000)
2821. feature "VLDLR" (0.000000)
2822. feature "C3orf70" (0.000000)
2823. feature "IER2" (0.000000)
2824. feature "PITPNM3" (0.000000)
2825. feature "BLOC1S5-TXNDC5" (0.000000)
2826. feature "ITGA8" (0.000000)
2827. feature "MSS51" (0.000000)
2828. feature "SMTNL2" (0.000000)
2829. feature "JADE1" (0.000000)
2830. feature "ZIC3" (0.000000)
2831. feature "CEBPG" (0.000000)
2832. feature "SERPINA1" (0.000000)
2833. feature "TOB1" (0.000000)
2834. feature "VSIG1" (0.000000)
2835. feature "DLK2" (0.000000)
2836. feature "TH" (0.000000)
2837. feature "C1QTNF12" (0.000000)
2838. feature "ACE2" (0.000000)
2839. feature "FAM110D" (0.000000)
2840. feature "PHF2P2" (0.000000)
2841. feature "SH2D5" (0.000000)
2842. feature "LARGE2" (0.000000)
2843. feature "WSB1" (0.000000)
2844. feature "TICRR" (0.000000)
2845. feature "CLK3" (0.000000)
2846. feature "CCNE1" (0.000000)
2847. feature "NCALD" (0.000000)
2848. feature "PTPRR" (0.000000)
2849. feature "HSPG2" (0.000000)
2850. feature "MCIDAS" (0.000000)
2851. feature "SLCO5A1" (0.000000)
2852. feature "DNMT1" (0.000000)
2853. feature "BOP1" (0.000000)
2854. feature "DNAH3" (0.000000)
2855. feature "OR7E22P" (0.000000)
2856. feature "SLC29A3" (0.000000)
2857. feature "GPR87" (0.000000)
2858. feature "TRIB1" (0.000000)
2859. feature "FRAS1" (0.000000)
2860. feature "DSC1" (0.000000)
2861. feature "PHF19" (0.000000)
2862. feature "IKBKE" (0.000000)
2863. feature "PKDCC" (0.000000)
2864. feature "LINC01686" (0.000000)
2865. feature "ELFN2" (0.000000)
2866. feature "LINC02878" (0.000000)
2867. feature "CELSR3" (0.000000)
2868. feature "USP27X-DT" (0.000000)
2869. feature "PLXNA1" (0.000000)
2870. feature "MB" (0.000000)
2871. feature "SORBS1" (0.000000)
2872. feature "RAB36" (0.000000)
2873. feature "TECTA" (0.000000)
2874. feature "PTPRU" (0.000000)
2875. feature "L3MBTL1" (0.000000)
2876. feature "CAMK1D" (0.000000)
2877. feature "C1orf127" (0.000000)
2878. feature "SLC9A7" (0.000000)
2879. feature "SPTBN4" (0.000000)
2880. feature "CACNA1I" (0.000000)
2881. feature "SCUBE3" (0.000000)
2882. feature "SLC9A7P1" (0.000000)
2883. feature "DMP1" (0.000000)
2884. feature "EXO1" (0.000000)
2885. feature "MAGED1" (0.000000)
2886. feature "EML2-AS1" (0.000000)
2887. feature "PINK1" (0.000000)
2888. feature "IFIT2" (0.000000)
2889. feature "PCDHGB8P" (0.000000)
2890. feature "STOX2" (0.000000)
2891. feature "EHD3" (0.000000)
2892. feature "SDR16C5" (0.000000)
2893. feature "LINC01185" (0.000000)
2894. feature "SYT16" (0.000000)
2895. feature "GUCY1A2" (0.000000)
2896. feature "PTK2B" (0.000000)
2897. feature "KIF12" (0.000000)
2898. feature "SV2B" (0.000000)
2899. feature "MT-ATP8" (0.000000)
2900. feature "INKA2-AS1" (0.000000)
2901. feature "CDC25A" (0.000000)
2902. feature "IPO4" (0.000000)
2903. feature "KCNK6" (0.000000)
2904. feature "SLC1A2" (0.000000)
2905. feature "E2F2" (0.000000)
2906. feature "NR2F2" (0.000000)
2907. feature "OSBPL7" (0.000000)
2908. feature "GIPR" (0.000000)
2909. feature "RIN1" (0.000000)
2910. feature "IGF1R" (0.000000)
2911. feature "ABCC10" (0.000000)
2912. feature "BVES" (0.000000)
2913. feature "ZBTB42" (0.000000)
2914. feature "FHOD3" (0.000000)
2915. feature "FAM171A1" (0.000000)
2916. feature "SOX9-AS1" (0.000000)
2917. feature "PLOD2" (0.000000)
2918. feature "ZNF343" (0.000000)
2919. feature "ST6GALNAC5" (0.000000)
2920. feature "TTC28" (0.000000)
2921. feature "TRPM6" (0.000000)
2922. feature "DISP1" (0.000000)
2923. feature "ADAMTS13" (0.000000)
2924. feature "RASIP1" (0.000000)
2925. feature "ODAPH" (0.000000)
2926. feature "ENO1P3" (0.000000)
2927. feature "ELAVL2" (0.000000)
2928. feature "SLC25A39" (0.000000)
2929. feature "IL27RA" (0.000000)
2930. feature "PPP1R14D" (0.000000)
2931. feature "TYRO3" (0.000000)
2932. feature "EVPLL" (0.000000)
2933. feature "ZSCAN18" (0.000000)
2934. feature "LGALS9" (0.000000)
2935. feature "MELK" (0.000000)
2936. feature "ATP6V1FNB" (0.000000)
2937. feature "ARMCX2" (0.000000)
2938. feature "ZNF827" (0.000000)
2939. feature "ACRBP" (0.000000)
2940. feature "DGKQ" (0.000000)
2941. feature "ARHGEF16" (0.000000)
2942. feature "KIF18B" (0.000000)
2943. feature "PAH" (0.000000)
2944. feature "SALL2" (0.000000)
2945. feature "NRP2" (0.000000)
2946. feature "MAFB" (0.000000)
2947. feature "BSCL2" (0.000000)
2948. feature "HS3ST1" (0.000000)
2949. feature "HES1" (0.000000)
2950. feature "H1-0" (0.000000)
2951. feature "HLA-DPA1" (0.000000)
2952. feature "ALDH1L2" (0.000000)
2953. feature "ZNF70" (0.000000)
2954. feature "KRT40" (0.000000)
2955. feature "FNDC5" (0.000000)
2956. feature "ANO7" (0.000000)
2957. feature "EYA4" (0.000000)
2958. feature "LINC01260" (0.000000)
2959. feature "TYK2" (0.000000)
2960. feature "SLC22A23" (0.000000)
2961. feature "KCNK15" (0.000000)
2962. feature "WNT6" (0.000000)
2963. feature "A4GALT" (0.000000)
2964. feature "ERG" (0.000000)
2965. feature "CTTNBP2" (0.000000)
2966. feature "KCNJ13" (0.000000)
2967. feature "GBP2" (0.000000)
2968. feature "ZC2HC1C" (0.000000)
2969. feature "LINC01119" (0.000000)
2970. feature "SNTB1" (0.000000)
2971. feature "LPCAT2" (0.000000)
2972. feature "H2AZ1" (0.000000)
2973. feature "AHR" (0.000000)
2974. feature "HLA-DRB6" (0.000000)
2975. feature "AMER1" (0.000000)
2976. feature "RASL10B" (0.000000)
2977. feature "SNTA1" (0.000000)
2978. feature "GPR199P" (0.000000)
2979. feature "RAET1L" (0.000000)
2980. feature "LINC00324" (0.000000)
2981. feature "POU2F2" (0.000000)
2982. feature "GPX2" (0.000000)
2983. feature "ZNF543" (0.000000)
2984. feature "SULT2B1" (0.000000)
2985. feature "CCR6" (0.000000)
2986. feature "KIF14" (0.000000)
2987. feature "LTBP1" (0.000000)
2988. feature "SH3BP5" (0.000000)
2989. feature "CYBA" (0.000000)
2990. feature "RMI2" (0.000000)
2991. feature "HGFAC" (0.000000)
2992. feature "COLQ" (0.000000)
2993. feature "HYAL2" (0.000000)
2994. feature "LINC02533" (0.000000)
2995. feature "CFB" (0.000000)
2996. feature "PPM1M" (0.000000)
2997. feature "AATBC" (0.000000)
2998. feature "SLC18B1" (0.000000)
2999. feature "LINC02532" (0.000000)
3000. feature "CYP1B1" (0.000000)
¶
4.1.3) XGBoost is not necessary since Test accuracy is already 1.0
¶
4.1.4) KNN
best_knn_SS_MCF7=train_and_evaluate_knn(X_train_SS_MCF, X_test_SS_MCF, y_train_SS_MCF, y_test_SS_MCF)
Best hyperparameters: {'n_neighbors': 3}
Accuracy: 1.0
F1: 1.0
Classification Report:
precision recall f1-score support
0.0 1.00 1.00 1.00 25
1.0 1.00 1.00 1.00 25
accuracy 1.00 50
macro avg 1.00 1.00 1.00 50
weighted avg 1.00 1.00 1.00 50
¶
4.1.5) Logistic Regression
best_logreg_SS_MCF7=train_and_evaluate_logreg(X_train_SS_MCF, X_test_SS_MCF, y_train_SS_MCF, y_test_SS_MCF)
Best hyperparameters: {'C': 0.002, 'penalty': 'l2'}
Accuracy: 1.0
F1: 1.0
Classification Report:
precision recall f1-score support
0.0 1.00 1.00 1.00 25
1.0 1.00 1.00 1.00 25
accuracy 1.00 50
macro avg 1.00 1.00 1.00 50
weighted avg 1.00 1.00 1.00 50
X_train_pca_SS_MCF, X_test_pca_SS_MCF, y_train_SS_MCF, y_test_SS_MCF,scaler,pca_SS_MCF = prepare_data_pca(df_trans_SS_MCF)
model_logreg_mc_SMART_ver2_SS_MCF=train_and_evaluate_logreg(X_train_pca_SS_MCF, X_test_pca_SS_MCF, y_train_SS_MCF, y_test_SS_MCF)
Best hyperparameters: {'C': 0.002, 'penalty': 'l2'}
Accuracy: 1.0
F1: 1.0
Classification Report:
precision recall f1-score support
0.0 1.00 1.00 1.00 25
1.0 1.00 1.00 1.00 25
accuracy 1.00 50
macro avg 1.00 1.00 1.00 50
weighted avg 1.00 1.00 1.00 50
¶
4.1.6) CatBoost
best_catboost_SS_MCF7=train_and_evaluate_catboost(X_train_SS_MCF, X_test_SS_MCF, y_train_SS_MCF, y_test_SS_MCF)
Best hyperparameters: {'logging_level': 'Silent'}
Accuracy: 1.0
F1: 1.0
Classification Report:
precision recall f1-score support
0.0 1.00 1.00 1.00 25
1.0 1.00 1.00 1.00 25
accuracy 1.00 50
macro avg 1.00 1.00 1.00 50
weighted avg 1.00 1.00 1.00 50
new_columns_names_SS_MCF = []
for i in range(1,3001):
name_new_SS_MCF = f'f{i}'
new_columns_names_SS_MCF.append(name_new_SS_MCF)
X_test_pd_SS_MCF=pd.DataFrame(X_test_SS_MCF, columns=new_columns_names_SS_MCF)
X_train_pd_SS_MCF=pd.DataFrame(X_train_SS_MCF, columns=new_columns_names_SS_MCF)
model_catboost_mc_SMART_ver2_SS_MCF, cols_cut_SS_MCF=train_and_evaluate_catboost2(X_train_pd_SS_MCF, X_test_pd_SS_MCF, y_train_SS_MCF, y_test_SS_MCF)
1304
Best hyperparameters: {'l2_leaf_reg': 3, 'logging_level': 'Silent', 'n_estimators': 500}
Accuracy: 1.0
F1: 1.0
Classification Report:
precision recall f1-score support
0.0 1.00 1.00 1.00 25
1.0 1.00 1.00 1.00 25
accuracy 1.00 50
macro avg 1.00 1.00 1.00 50
weighted avg 1.00 1.00 1.00 50
¶
4.2) PREDICTORS SMARTSEQ HCC1806
We apply the same steps of the dataset before (see 4.1) to the SmartSeq HCC1806 dataset
#load the SmartSeq HCC1806 dataset
df_SS_HCC1806_norm_filt = pd.read_csv("SmartSeq\HCC1806_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)
df_SS_HCC1806_norm_filtT = df_SS_HCC1806_norm_filt.T
# Add the target column
df_SS_HCC1806_norm_filtT['target'] = np.where(df_SS_HCC1806_norm_filtT.index.str.contains('Hypo'), 1, 0)
#i want to split the data into training and testing data, with a balance of 70% training and 30% testing
X_SS_HCC1806 = df_SS_HCC1806_norm_filtT.drop('target', axis=1)
y_SS_HCC1806 = df_SS_HCC1806_norm_filtT['target']
X_train_df_SS_HCC1806, X_test_df_SS_HCC1806, y_train_df_SS_HCC1806, y_test_df_SS_HCC1806 = train_test_split(X_SS_HCC1806, y_SS_HCC1806, test_size=0.3, random_state=42)
df_basic_SS_HCC=df_SS_HCC1806_norm_filt
df_trans_SS_HCC=df_basic_SS_HCC.T
df_trans_SS_HCC['TARGET'] = df_trans_SS_HCC.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_SS_HCC.loc[df_trans_SS_HCC.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_SS_HCC.loc[df_trans_SS_HCC.index.str.contains('Hypo'), 'TARGET'] = 1
X_train_SS_HCC, X_test_SS_HCC, y_train_SS_HCC, y_test_SS_HCC,scaler_SS_HCC = prepare_data(df_trans_SS_HCC)
¶
4.2.1) SVM
We used same approach used for SmartSeq MCF7, see 4.1.1.
# build SVM on SmartSeq HCC1806
# Define the SVM classifier
svm = SVC(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'kernel': ['linear', 'rbf']}
grid_search_svm = GridSearchCV(svm, param_grid, cv=5)
# Define the leave one out cross validation
#loo = LeaveOneOut()
# Perform the nested cross validation
scores_svm = cross_val_score(grid_search_svm, X_train_df_SS_HCC1806, y_train_df_SS_HCC1806, cv=5)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of 5 fold nested cross validation: ", np.mean(scores_svm))
# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search_svm.fit(X_train_df_SS_HCC1806, y_train_df_SS_HCC1806)
print("Best kernel: ", grid_search_svm.best_params_)
print("Best accuracy: ", grid_search_svm.best_score_)
best_svm_SS_HCC1806 = grid_search_svm.best_estimator_
print("Best model: ", best_svm_SS_HCC1806)
# Test the model on the test dataset
print("Test accuracy: ", best_svm_SS_HCC1806.score(X_test_df_SS_HCC1806, y_test_df_SS_HCC1806))
Mean accuracy of 5 fold nested cross validation: 0.9538461538461538
Best kernel: {'kernel': 'linear'}
Best accuracy: 0.9846153846153847
Best model: SVC(kernel='linear', random_state=42)
Test accuracy: 1.0
¶
4.2.2) Random Forest
Here we build the random forest classifier for the SmartSeq HCC1806 dataset following exactly the same structure as before.
We notice that in this case the performance is a little lower with respect to the other one as this estimator has a perfomance approximately of 98.18%.
#i build the random forest classifier for the SmartSeq HCC1806 dataset
# Define the random forest classifier
rf = RandomForestClassifier(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [10, 20, 50], 'max_depth': [10, 20, 30]}
grid_search = GridSearchCV(rf, param_grid, cv=5)
# Define the leave one out cross validation
loo = LeaveOneOut()
# Perform the nested cross validation
scores = cross_val_score(grid_search, X_train_df_SS_HCC1806, y_train_df_SS_HCC1806, cv=loo)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))
# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_SS_HCC1806, y_train_df_SS_HCC1806)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)
#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_SS_HCC1806, y_test_df_SS_HCC1806))
# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_SS_HCC1806.shape[1]):
print("%d. feature %s (%f)" % (f + 1, X_train_df_SS_HCC1806.columns[indices[f]], importances[indices[f]]))
best_rf_SS_HCC1806 = grid_search.best_estimator_
Mean accuracy of nested cross validation: 0.984251968503937
Best hyperparameters: {'max_depth': 10, 'n_estimators': 50}
Best accuracy: 0.9846153846153847
Best model: RandomForestClassifier(max_depth=10, n_estimators=50, random_state=42)
Test accuracy: 0.9818181818181818
Feature ranking:
1. feature "BNIP3" (0.051024)
2. feature "NDRG1" (0.049045)
3. feature "FAM162A" (0.042764)
4. feature "BNIP3L" (0.029959)
5. feature "LOXL2" (0.026526)
6. feature "CA9" (0.024664)
7. feature "ANGPTL4" (0.021798)
8. feature "BLCAP" (0.021322)
9. feature "PFKFB3" (0.020018)
10. feature "DDIT4" (0.019817)
11. feature "ALDOA" (0.018799)
12. feature "STC2" (0.018340)
13. feature "LDHA" (0.017305)
14. feature "ERO1A" (0.017265)
15. feature "PLOD2" (0.017054)
16. feature "ALDOC" (0.016458)
17. feature "BHLHE40" (0.016411)
18. feature "HMGCR" (0.016001)
19. feature "SLC2A1" (0.015453)
20. feature "INSIG1" (0.014220)
21. feature "PLAC8" (0.014200)
22. feature "MIF" (0.014096)
23. feature "ENO2" (0.013592)
24. feature "EGLN1" (0.013477)
25. feature "PLIN2" (0.011913)
26. feature "PPP1R3G" (0.011837)
27. feature "EIF5" (0.011658)
28. feature "EGLN3" (0.011127)
29. feature "SNX33" (0.010708)
30. feature "P4HA1" (0.009833)
31. feature "H1-0" (0.009441)
32. feature "FSCN1" (0.009288)
33. feature "GPI" (0.009261)
34. feature "PGK1" (0.008304)
35. feature "HES1" (0.008192)
36. feature "SLC38A2" (0.007999)
37. feature "LBH" (0.006475)
38. feature "MIF-AS1" (0.006367)
39. feature "COL6A1" (0.006199)
40. feature "AREG" (0.006158)
41. feature "MOB3A" (0.006005)
42. feature "UNC13D" (0.005927)
43. feature "MUC20-OT1" (0.005553)
44. feature "C4orf3" (0.005547)
45. feature "NEDD9" (0.005483)
46. feature "FUT11" (0.005374)
47. feature "DKK1" (0.005246)
48. feature "DHCR24" (0.004993)
49. feature "IER2" (0.004860)
50. feature "GYS1" (0.004837)
51. feature "L1CAM" (0.004722)
52. feature "NQO1" (0.004721)
53. feature "FAM13A" (0.004630)
54. feature "HK2" (0.004443)
55. feature "GRN" (0.004023)
56. feature "PFKFB4" (0.003864)
57. feature "ZWINT" (0.003773)
58. feature "DEGS2" (0.003706)
59. feature "FXYD3" (0.003678)
60. feature "PSMD2" (0.003654)
61. feature "DHRS3" (0.003509)
62. feature "SRXN1" (0.003453)
63. feature "CD69" (0.003432)
64. feature "LAMP3" (0.003361)
65. feature "DUSP1" (0.003243)
66. feature "KDM5B" (0.003218)
67. feature "CDH1" (0.003207)
68. feature "TMEM45A" (0.003073)
69. feature "TTK" (0.003028)
70. feature "CCN1" (0.003022)
71. feature "CDK2AP2" (0.003005)
72. feature "HBP1" (0.002978)
73. feature "ZDHHC23" (0.002874)
74. feature "CALHM2" (0.002822)
75. feature "MT-CYB" (0.002776)
76. feature "KYNU" (0.002742)
77. feature "NOL6" (0.002672)
78. feature "AHNAK" (0.002667)
79. feature "FOSL2" (0.002662)
80. feature "IGFBP3" (0.002506)
81. feature "HERPUD1" (0.002472)
82. feature "CSTB" (0.002384)
83. feature "RPLP0P2" (0.002362)
84. feature "EXPH5" (0.002324)
85. feature "UBE2C" (0.002324)
86. feature "CKS2" (0.002256)
87. feature "ITGB5" (0.002255)
88. feature "KCTD11" (0.002243)
89. feature "BTN2A1" (0.002181)
90. feature "MT-ND4" (0.002156)
91. feature "SFN" (0.002087)
92. feature "PAM" (0.001851)
93. feature "TTC28" (0.001836)
94. feature "C10orf55" (0.001825)
95. feature "RPL10P12" (0.001824)
96. feature "LINC02614" (0.001801)
97. feature "AKR1B1" (0.001800)
98. feature "UGDH" (0.001760)
99. feature "PHLDA1-AS1" (0.001757)
100. feature "IFIT2" (0.001731)
101. feature "KNSTRN" (0.001713)
102. feature "UBE2S" (0.001709)
103. feature "DLSTP1" (0.001706)
104. feature "PAIP1P1" (0.001704)
105. feature "PLK3" (0.001702)
106. feature "AGR2" (0.001689)
107. feature "PLK2" (0.001664)
108. feature "NLRP10" (0.001625)
109. feature "HSPB8" (0.001621)
110. feature "ESPL1" (0.001565)
111. feature "LEAP2" (0.001532)
112. feature "SLCO4A1" (0.001519)
113. feature "NFKBIZ" (0.001502)
114. feature "RIMKLA" (0.001456)
115. feature "HCP5" (0.001374)
116. feature "AZIN2" (0.001360)
117. feature "GLCCI1" (0.001357)
118. feature "LTBP2" (0.001328)
119. feature "TXNRD1" (0.001293)
120. feature "GALNT5" (0.001282)
121. feature "DOCK10" (0.001261)
122. feature "PHF19" (0.001228)
123. feature "NOLC1" (0.001224)
124. feature "H4C3" (0.001218)
125. feature "CPLANE2" (0.001218)
126. feature "TSC22D1" (0.001217)
127. feature "KIF22" (0.001214)
128. feature "ZNF433" (0.001208)
129. feature "BEST1" (0.001193)
130. feature "UGT1A10" (0.001190)
131. feature "IL1A" (0.001182)
132. feature "CRY2" (0.001181)
133. feature "AVPI1" (0.001177)
134. feature "NUDT11" (0.001174)
135. feature "NUCB2" (0.001174)
136. feature "SPRYD3" (0.001172)
137. feature "ADM" (0.001167)
138. feature "JDP2-AS1" (0.001159)
139. feature "PYGB" (0.001156)
140. feature "PRSS23" (0.001143)
141. feature "TMPRSS13" (0.001124)
142. feature "ASF1B" (0.001121)
143. feature "FSIP2" (0.001100)
144. feature "ADGRG2" (0.001090)
145. feature "HMGA1" (0.001088)
146. feature "FBXO2" (0.001080)
147. feature "PPP2R2B" (0.001067)
148. feature "BBS12" (0.001066)
149. feature "TACSTD2" (0.001045)
150. feature "BCL11B" (0.001042)
151. feature "HTR7" (0.001002)
152. feature "ID3" (0.000985)
153. feature "ENTPD3" (0.000982)
154. feature "MYBL2" (0.000974)
155. feature "RND3" (0.000958)
156. feature "PRKG2" (0.000950)
157. feature "PKP1" (0.000945)
158. feature "ZBTB3" (0.000907)
159. feature "JUP" (0.000907)
160. feature "MT1X" (0.000828)
161. feature "KDM6A" (0.000817)
162. feature "MAL2" (0.000787)
163. feature "CCDC87" (0.000756)
164. feature "CUL7" (0.000663)
165. feature "ENPP5" (0.000648)
166. feature "RNASE7" (0.000642)
167. feature "NUPR1" (0.000629)
168. feature "PTP4A1" (0.000629)
169. feature "CNIH3" (0.000628)
170. feature "ACTG1P10" (0.000628)
171. feature "SCARA3" (0.000626)
172. feature "INSYN2B" (0.000625)
173. feature "HTR2B" (0.000624)
174. feature "KCTD8" (0.000623)
175. feature "TMSB15A" (0.000622)
176. feature "FGFBP1" (0.000621)
177. feature "PCDH10" (0.000621)
178. feature "MCM10" (0.000621)
179. feature "ZNF182" (0.000621)
180. feature "CLIC2" (0.000621)
181. feature "CCNB1" (0.000620)
182. feature "SCD5" (0.000619)
183. feature "KRTAP2-3" (0.000619)
184. feature "APOE" (0.000619)
185. feature "S100A16" (0.000619)
186. feature "MOCS1" (0.000619)
187. feature "MAPK8IP2" (0.000619)
188. feature "ELMO3" (0.000619)
189. feature "KLK8" (0.000619)
190. feature "TP53INP1" (0.000619)
191. feature "ZNF227" (0.000619)
192. feature "VWA5A" (0.000618)
193. feature "CLIC3" (0.000618)
194. feature "ARRDC3" (0.000618)
195. feature "CTSE" (0.000617)
196. feature "NRG1" (0.000616)
197. feature "TBX5" (0.000616)
198. feature "EIF2AK3-DT" (0.000616)
199. feature "KLF6" (0.000615)
200. feature "SERPINE1" (0.000614)
201. feature "MT-CO3" (0.000614)
202. feature "CCNB3" (0.000613)
203. feature "ABCA3" (0.000613)
204. feature "DGKI" (0.000612)
205. feature "CRKL" (0.000611)
206. feature "ASS1P12" (0.000609)
207. feature "CAPN5" (0.000608)
208. feature "TRAM1L1" (0.000606)
209. feature "REEP1" (0.000602)
210. feature "KRT5" (0.000601)
211. feature "TAGLN" (0.000598)
212. feature "TNFRSF21" (0.000598)
213. feature "CD99L2" (0.000597)
214. feature "OLR1" (0.000597)
215. feature "SHCBP1" (0.000597)
216. feature "TMEM45B" (0.000596)
217. feature "ADIRF-AS1" (0.000595)
218. feature "PLAUR" (0.000590)
219. feature "TATDN2P2" (0.000586)
220. feature "FHL1" (0.000586)
221. feature "PCNA" (0.000583)
222. feature "CPEB2" (0.000578)
223. feature "MRNIP" (0.000577)
224. feature "SLC7A11" (0.000571)
225. feature "ABCA1" (0.000567)
226. feature "SLC43A3" (0.000567)
227. feature "SERPINI1" (0.000564)
228. feature "IGFL2-AS1" (0.000564)
229. feature "SLC7A2" (0.000561)
230. feature "ARC" (0.000554)
231. feature "MOV10L1" (0.000539)
232. feature "MIR34AHG" (0.000525)
233. feature "LARP4P" (0.000525)
234. feature "SERPINB11" (0.000509)
235. feature "GNMT" (0.000508)
236. feature "ITGA2" (0.000505)
237. feature "AQP3" (0.000504)
238. feature "TSNARE1" (0.000504)
239. feature "ZNF470" (0.000504)
240. feature "B3GNT3" (0.000488)
241. feature "LIG1" (0.000477)
242. feature "H4C8" (0.000473)
243. feature "FOS" (0.000472)
244. feature "FOXD2" (0.000472)
245. feature "SYNGR3" (0.000424)
246. feature "NDC80" (0.000423)
247. feature "CENPF" (0.000422)
248. feature "ENPP2" (0.000422)
249. feature "ISL2" (0.000420)
250. feature "CALB1" (0.000321)
251. feature "DEPTOR" (0.000316)
252. feature "HES5" (0.000315)
253. feature "PTGES" (0.000306)
254. feature "HPGD" (0.000299)
255. feature "SH3TC2-DT" (0.000299)
256. feature "POLD1" (0.000195)
257. feature "ZNF853" (0.000000)
258. feature "IFIT1" (0.000000)
259. feature "PELI2" (0.000000)
260. feature "KRT13" (0.000000)
261. feature "ZSWIM3" (0.000000)
262. feature "IQGAP2" (0.000000)
263. feature "CAND2" (0.000000)
264. feature "KRT7" (0.000000)
265. feature "NPR3" (0.000000)
266. feature "MC1R" (0.000000)
267. feature "SMIM5" (0.000000)
268. feature "GDPD3" (0.000000)
269. feature "ERICH5" (0.000000)
270. feature "DGAT2" (0.000000)
271. feature "ABAT" (0.000000)
272. feature "LINC00161" (0.000000)
273. feature "SIGLEC15" (0.000000)
274. feature "EDIL3" (0.000000)
275. feature "AURKC" (0.000000)
276. feature "PACSIN1" (0.000000)
277. feature "ZFP36L1" (0.000000)
278. feature "MVP" (0.000000)
279. feature "PPP4R4" (0.000000)
280. feature "CDC45" (0.000000)
281. feature "EPHA10" (0.000000)
282. feature "UNC13B" (0.000000)
283. feature "MAG" (0.000000)
284. feature "TCHH" (0.000000)
285. feature "DNAJB5" (0.000000)
286. feature "PPP1R36" (0.000000)
287. feature "THSD7A" (0.000000)
288. feature "AGMO" (0.000000)
289. feature "ZBTB26" (0.000000)
290. feature "CXCL2" (0.000000)
291. feature "KCNIP3" (0.000000)
292. feature "THOC1-DT" (0.000000)
293. feature "EFEMP1" (0.000000)
294. feature "ZNF425" (0.000000)
295. feature "RASL10A" (0.000000)
296. feature "SESN2" (0.000000)
297. feature "VAMP1" (0.000000)
298. feature "ANO9" (0.000000)
299. feature "IL17RB" (0.000000)
300. feature "KRT17" (0.000000)
301. feature "CMYA5" (0.000000)
302. feature "CYP26C1" (0.000000)
303. feature "STAT4" (0.000000)
304. feature "PADI1" (0.000000)
305. feature "SOWAHA" (0.000000)
306. feature "GLRA3" (0.000000)
307. feature "PLA2G4D" (0.000000)
308. feature "ARRDC4" (0.000000)
309. feature "MSMO1" (0.000000)
310. feature "TUBA8" (0.000000)
311. feature "MMP1" (0.000000)
312. feature "GLUL" (0.000000)
313. feature "MUC1" (0.000000)
314. feature "MMP13" (0.000000)
315. feature "SLC30A3" (0.000000)
316. feature "TFRC" (0.000000)
317. feature "IGFBP1" (0.000000)
318. feature "GCSAM" (0.000000)
319. feature "ADCY6" (0.000000)
320. feature "LRRC2" (0.000000)
321. feature "SNCG" (0.000000)
322. feature "ZNF311" (0.000000)
323. feature "TMEM187" (0.000000)
324. feature "PDE2A" (0.000000)
325. feature "STOX1" (0.000000)
326. feature "LINC00235" (0.000000)
327. feature "DGCR5" (0.000000)
328. feature "CPE" (0.000000)
329. feature "PLB1" (0.000000)
330. feature "ZBED6CL" (0.000000)
331. feature "COBL" (0.000000)
332. feature "NKX6-1" (0.000000)
333. feature "BST2" (0.000000)
334. feature "TTC23L-AS1" (0.000000)
335. feature "SYNM" (0.000000)
336. feature "POU3F4" (0.000000)
337. feature "ZNF878" (0.000000)
338. feature "MYO3A" (0.000000)
339. feature "OSBP2" (0.000000)
340. feature "RTN4RL1" (0.000000)
341. feature "ADGRF1" (0.000000)
342. feature "IDUA" (0.000000)
343. feature "SYT3" (0.000000)
344. feature "CPNE5" (0.000000)
345. feature "C16orf86" (0.000000)
346. feature "RIPK3" (0.000000)
347. feature "ACADL" (0.000000)
348. feature "VIT" (0.000000)
349. feature "ABCA6" (0.000000)
350. feature "KIAA1549" (0.000000)
351. feature "TGFBR2" (0.000000)
352. feature "CPQ" (0.000000)
353. feature "GLDC" (0.000000)
354. feature "HECW2" (0.000000)
355. feature "HAP1" (0.000000)
356. feature "NR4A1" (0.000000)
357. feature "FAT4" (0.000000)
358. feature "RP1L1" (0.000000)
359. feature "FN1" (0.000000)
360. feature "F2RL3" (0.000000)
361. feature "GABRD" (0.000000)
362. feature "SCIN" (0.000000)
363. feature "AFM" (0.000000)
364. feature "ANLN" (0.000000)
365. feature "NOTCH3" (0.000000)
366. feature "CKAP2" (0.000000)
367. feature "PPM1M" (0.000000)
368. feature "CENPA" (0.000000)
369. feature "LYSMD1" (0.000000)
370. feature "ADD2" (0.000000)
371. feature "NTNG1" (0.000000)
372. feature "FEZF1" (0.000000)
373. feature "ZNF737" (0.000000)
374. feature "GHDC" (0.000000)
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2868. feature "DCDC2" (0.000000)
2869. feature "GUCY1A1" (0.000000)
2870. feature "SERPINB1" (0.000000)
2871. feature "SCNN1A" (0.000000)
2872. feature "MIGA2" (0.000000)
2873. feature "ZNF571" (0.000000)
2874. feature "ZNF442" (0.000000)
2875. feature "ATP6V0D1-DT" (0.000000)
2876. feature "RPSAP36" (0.000000)
2877. feature "GPR155" (0.000000)
2878. feature "TOMT" (0.000000)
2879. feature "LINC01460" (0.000000)
2880. feature "SLC22A20P" (0.000000)
2881. feature "MSH6" (0.000000)
2882. feature "MLXP1" (0.000000)
2883. feature "CRYBG2" (0.000000)
2884. feature "FEN1" (0.000000)
2885. feature "ACTG1P24" (0.000000)
2886. feature "SOCS2-AS1" (0.000000)
2887. feature "S1PR1" (0.000000)
2888. feature "SLC47A2" (0.000000)
2889. feature "RPL18AP7" (0.000000)
2890. feature "ATP2A3" (0.000000)
2891. feature "KRT18P59" (0.000000)
2892. feature "ALDH1A2" (0.000000)
2893. feature "NKD2" (0.000000)
2894. feature "DNAJC6" (0.000000)
2895. feature "HPN" (0.000000)
2896. feature "MCM7" (0.000000)
2897. feature "GASK1B" (0.000000)
2898. feature "AMOTL2" (0.000000)
2899. feature "GPRASP1" (0.000000)
2900. feature "GTSE1" (0.000000)
2901. feature "CHST13" (0.000000)
2902. feature "TBKBP1" (0.000000)
2903. feature "KCNK12" (0.000000)
2904. feature "NEIL3" (0.000000)
2905. feature "NUAK2" (0.000000)
2906. feature "FHIP2B" (0.000000)
2907. feature "ZBTB49" (0.000000)
2908. feature "ATP9B" (0.000000)
2909. feature "MYCL" (0.000000)
2910. feature "MATN3" (0.000000)
2911. feature "VAMP5" (0.000000)
2912. feature "MAP4K1" (0.000000)
2913. feature "SH2D2A" (0.000000)
2914. feature "CCDC96" (0.000000)
2915. feature "RHBG" (0.000000)
2916. feature "YPEL5" (0.000000)
2917. feature "ZC3H12A" (0.000000)
2918. feature "RIMS3" (0.000000)
2919. feature "LINC01752" (0.000000)
2920. feature "SLAMF8" (0.000000)
2921. feature "TUBA1B" (0.000000)
2922. feature "PCED1B-AS1" (0.000000)
2923. feature "FABP3" (0.000000)
2924. feature "PIGZ" (0.000000)
2925. feature "BCRP9" (0.000000)
2926. feature "TDRKH-AS1" (0.000000)
2927. feature "ZNF473" (0.000000)
2928. feature "PPM1L" (0.000000)
2929. feature "TMEM249" (0.000000)
2930. feature "ADCY10" (0.000000)
2931. feature "LINC01956" (0.000000)
2932. feature "PLEKHA7" (0.000000)
2933. feature "LINC02085" (0.000000)
2934. feature "MSC" (0.000000)
2935. feature "KRT80" (0.000000)
2936. feature "CCK" (0.000000)
2937. feature "PALM2AKAP2" (0.000000)
2938. feature "GUCY1B1" (0.000000)
2939. feature "ZKSCAN3" (0.000000)
2940. feature "PAX7" (0.000000)
2941. feature "LINC01270" (0.000000)
2942. feature "CDCA3" (0.000000)
2943. feature "KLHL22" (0.000000)
2944. feature "SLIT1" (0.000000)
2945. feature "TP53I11" (0.000000)
2946. feature "FTCDNL1" (0.000000)
2947. feature "HLA-DOB" (0.000000)
2948. feature "TRPV4" (0.000000)
2949. feature "CAMK1D" (0.000000)
2950. feature "CRABP2" (0.000000)
2951. feature "LYPD5" (0.000000)
2952. feature "ZNF517" (0.000000)
2953. feature "HLX" (0.000000)
2954. feature "SERPINF2" (0.000000)
2955. feature "CYYR1" (0.000000)
2956. feature "SLC1A3" (0.000000)
2957. feature "FABP5" (0.000000)
2958. feature "FBXO43" (0.000000)
2959. feature "ZBTB10" (0.000000)
2960. feature "FGFR1" (0.000000)
2961. feature "LINC00921" (0.000000)
2962. feature "OXTR" (0.000000)
2963. feature "COX6B2" (0.000000)
2964. feature "TM4SF1" (0.000000)
2965. feature "SCIRT" (0.000000)
2966. feature "PTPRN" (0.000000)
2967. feature "PLAC8L1" (0.000000)
2968. feature "IGFL1" (0.000000)
2969. feature "FCER1G" (0.000000)
2970. feature "PEAR1" (0.000000)
2971. feature "NAV1" (0.000000)
2972. feature "ZSCAN30" (0.000000)
2973. feature "RAPGEF3" (0.000000)
2974. feature "C19orf48" (0.000000)
2975. feature "KIF6" (0.000000)
2976. feature "TNFRSF10D" (0.000000)
2977. feature "VIPR1-AS1" (0.000000)
2978. feature "PATL2" (0.000000)
2979. feature "LIMS2" (0.000000)
2980. feature "LINC01203" (0.000000)
2981. feature "KCNK2" (0.000000)
2982. feature "RAMP2" (0.000000)
2983. feature "OTULIN-DT" (0.000000)
2984. feature "DCLK1" (0.000000)
2985. feature "NPTN-IT1" (0.000000)
2986. feature "C11orf87" (0.000000)
2987. feature "ZNF214" (0.000000)
2988. feature "TSSK4" (0.000000)
2989. feature "APCDD1L" (0.000000)
2990. feature "TLR4" (0.000000)
2991. feature "COL9A2" (0.000000)
2992. feature "WARS1" (0.000000)
2993. feature "FAM47E" (0.000000)
2994. feature "EFNB3" (0.000000)
2995. feature "HMCN1" (0.000000)
2996. feature "FTLP12" (0.000000)
2997. feature "NINJ2" (0.000000)
2998. feature "E2F1" (0.000000)
2999. feature "MYH9" (0.000000)
3000. feature "SEC14L1P1" (0.000000)
¶
4.2.3) XGBoost
Since also experimenting with other hyperparameters (that are not show in the code before to maintain an acceptable running time) have not lead us to performance improving, we decide to experiment with the XGboost classifier.
XGBoost is an ensemble learning method similar to the random forest but it is specifically based on gradient boosting. It builds an ensemble of weak learners (usually and also in our case decision trees) sequentially, with each new learner focused on correcting the errors of the previous ones.
XGBoost often achieves even higher prediction accuracy compared to Random Forest, especially for structured/tabular data. It can effectively capture complex relationships between features and the target variable through gradient boosting and optimization techniques.
This was confirmed from the fact that the Xgboost classifier got a 100% score on the test dataset.
Comparing the list of most important features, we can see some differences in the first 20 places that have surely made this estimator perform better then the one before.
#build the XGBoost classifier for the SmartSeq HCC1806 dataset
from xgboost import XGBClassifier
# Define the XGBoost classifier
xgb = XGBClassifier(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [10, 20, 50], 'max_depth': [10, 20, 30]}
grid_search = GridSearchCV(xgb, param_grid, cv=5)
# Define the leave one out cross validation
kfold = KFold(n_splits=5, shuffle=True, random_state=42)
# Perform the nested cross validation
scores = cross_val_score(grid_search, X_train_df_SS_HCC1806, y_train_df_SS_HCC1806, cv=kfold)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))
# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_SS_HCC1806, y_train_df_SS_HCC1806)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)
#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_SS_HCC1806, y_test_df_SS_HCC1806))
# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_SS_HCC1806.shape[1]):
print("%d. feature %s (%f)" % (f + 1, X_train_df_SS_HCC1806.columns[indices[f]], importances[indices[f]]))
best_xgb_SS_HCC1806 = grid_search.best_estimator_
Mean accuracy of nested cross validation: 0.960923076923077
Best hyperparameters: {'max_depth': 10, 'n_estimators': 10}
Best accuracy: 0.9378461538461538
Best model: XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=10, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=10, n_jobs=None,
num_parallel_tree=None, random_state=42, ...)
Test accuracy: 1.0
Feature ranking:
1. feature "NDRG1" (0.427974)
2. feature "BNIP3" (0.196460)
3. feature "PGK1" (0.130549)
4. feature "EGLN3" (0.054734)
5. feature "BHLHE40" (0.045408)
6. feature "FUT11" (0.026541)
7. feature "NEDD9" (0.024163)
8. feature "UBE2C" (0.022874)
9. feature "CA9" (0.020198)
10. feature "ENO2" (0.010031)
11. feature "PRDM1" (0.009949)
12. feature "SERPINE1" (0.007386)
13. feature "MIF-AS1" (0.006251)
14. feature "KDM5B" (0.005265)
15. feature "PLIN2" (0.003634)
16. feature "G0S2" (0.003282)
17. feature "ARL6IP1" (0.003138)
18. feature "CCNB1" (0.001757)
19. feature "S100A2" (0.000332)
20. feature "HTRA1" (0.000073)
21. feature "RAET1L" (0.000000)
22. feature "C7" (0.000000)
23. feature "AGTR1" (0.000000)
24. feature "KLF6" (0.000000)
25. feature "RP1L1" (0.000000)
26. feature "PPM1J" (0.000000)
27. feature "FAT4" (0.000000)
28. feature "GLUL" (0.000000)
29. feature "FAM25A" (0.000000)
30. feature "ZNF737" (0.000000)
31. feature "FEZF1" (0.000000)
32. feature "NTNG1" (0.000000)
33. feature "ADD2" (0.000000)
34. feature "HECW2" (0.000000)
35. feature "HAP1" (0.000000)
36. feature "NR4A1" (0.000000)
37. feature "GLDC" (0.000000)
38. feature "ODAD2" (0.000000)
39. feature "SLC2A10" (0.000000)
40. feature "LINC01843" (0.000000)
41. feature "ADCY6" (0.000000)
42. feature "LRRC2" (0.000000)
43. feature "SNCG" (0.000000)
44. feature "MMP13" (0.000000)
45. feature "ZNF311" (0.000000)
46. feature "PDE2A" (0.000000)
47. feature "STOX1" (0.000000)
48. feature "LINC00235" (0.000000)
49. feature "DGCR5" (0.000000)
50. feature "CPE" (0.000000)
51. feature "PLB1" (0.000000)
52. feature "ZBED6CL" (0.000000)
53. feature "TMEM187" (0.000000)
54. feature "TGFBR2" (0.000000)
55. feature "SCIN" (0.000000)
56. feature "BMP4" (0.000000)
57. feature "ZNF563" (0.000000)
58. feature "RACGAP1" (0.000000)
59. feature "ENPP5" (0.000000)
60. feature "GABRD" (0.000000)
61. feature "LINC00628" (0.000000)
62. feature "DNAJC22" (0.000000)
63. feature "MUC1" (0.000000)
64. feature "ANLN" (0.000000)
65. feature "IQGAP2" (0.000000)
66. feature "EPHA10" (0.000000)
67. feature "VAMP1" (0.000000)
68. feature "TUBA8" (0.000000)
69. feature "MSMO1" (0.000000)
70. feature "PLA2G4D" (0.000000)
71. feature "GLRA3" (0.000000)
72. feature "SOWAHA" (0.000000)
73. feature "PADI1" (0.000000)
74. feature "STAT4" (0.000000)
75. feature "CYP26C1" (0.000000)
76. feature "CMYA5" (0.000000)
77. feature "ANO9" (0.000000)
78. feature "UNC13B" (0.000000)
79. feature "RASL10A" (0.000000)
80. feature "KCNIP3" (0.000000)
81. feature "CXCL2" (0.000000)
82. feature "DNAJB5" (0.000000)
83. feature "ARRDC4" (0.000000)
84. feature "CDC45" (0.000000)
85. feature "CAND2" (0.000000)
86. feature "PELI2" (0.000000)
87. feature "NOTCH3" (0.000000)
88. feature "IFIT1" (0.000000)
89. feature "CKAP2" (0.000000)
90. feature "PPM1M" (0.000000)
91. feature "CENPA" (0.000000)
92. feature "CPQ" (0.000000)
93. feature "TFRC" (0.000000)
94. feature "ZNF425" (0.000000)
95. feature "MVP" (0.000000)
96. feature "PACSIN1" (0.000000)
97. feature "AURKC" (0.000000)
98. feature "EDIL3" (0.000000)
99. feature "SIGLEC15" (0.000000)
100. feature "LINC00161" (0.000000)
101. feature "ABAT" (0.000000)
102. feature "MC1R" (0.000000)
103. feature "ERICH5" (0.000000)
104. feature "SRXN1" (0.000000)
105. feature "ZNF853" (0.000000)
106. feature "ERO1A" (0.000000)
107. feature "NPR3" (0.000000)
108. feature "IGFBP1" (0.000000)
109. feature "AQP3" (0.000000)
110. feature "SLC30A3" (0.000000)
111. feature "EXOC3L4" (0.000000)
112. feature "MME" (0.000000)
113. feature "NACAD" (0.000000)
114. feature "MMP2" (0.000000)
115. feature "TMCC2" (0.000000)
116. feature "AATBC" (0.000000)
117. feature "DISC1" (0.000000)
118. feature "CALR3" (0.000000)
119. feature "CTH" (0.000000)
120. feature "LINC02154" (0.000000)
121. feature "CLCA4" (0.000000)
122. feature "SMURF2" (0.000000)
123. feature "PIM1" (0.000000)
124. feature "GSC" (0.000000)
125. feature "ENTPD3" (0.000000)
126. feature "DSC2" (0.000000)
127. feature "DOK3" (0.000000)
128. feature "TRIM34" (0.000000)
129. feature "LIFR-AS1" (0.000000)
130. feature "SSTR2" (0.000000)
131. feature "NAT8L" (0.000000)
132. feature "FAM27E5" (0.000000)
133. feature "IGSF9B" (0.000000)
134. feature "COBL" (0.000000)
135. feature "DDO" (0.000000)
136. feature "PLAAT2" (0.000000)
137. feature "KRT18P63" (0.000000)
138. feature "LINC00865" (0.000000)
139. feature "ADRB2" (0.000000)
140. feature "MTNR1A" (0.000000)
141. feature "PSAP" (0.000000)
142. feature "FGF1" (0.000000)
143. feature "AK5" (0.000000)
144. feature "RNF180" (0.000000)
145. feature "FBN1" (0.000000)
146. feature "SNAI3" (0.000000)
147. feature "ZNF319" (0.000000)
148. feature "EIF5" (0.000000)
149. feature "UHRF1" (0.000000)
150. feature "WDR81" (0.000000)
151. feature "ITGA6" (0.000000)
152. feature "GBP6" (0.000000)
153. feature "FYN" (0.000000)
154. feature "SLC19A3" (0.000000)
155. feature "DMBX1" (0.000000)
156. feature "AOAH" (0.000000)
157. feature "DSCAM" (0.000000)
158. feature "OTUB2" (0.000000)
159. feature "SYT3" (0.000000)
160. feature "CEP83-DT" (0.000000)
161. feature "BEST3" (0.000000)
162. feature "ZNF610" (0.000000)
163. feature "ZNF415" (0.000000)
164. feature "GBP2" (0.000000)
165. feature "NPNT" (0.000000)
166. feature "POU3F4" (0.000000)
167. feature "OSBP2" (0.000000)
168. feature "PKP1" (0.000000)
169. feature "CPNE5" (0.000000)
170. feature "CASC19" (0.000000)
171. feature "C16orf86" (0.000000)
172. feature "RIPK3" (0.000000)
173. feature "ACADL" (0.000000)
174. feature "VIT" (0.000000)
175. feature "THSD7A" (0.000000)
176. feature "ABCA6" (0.000000)
177. feature "TTC23L-AS1" (0.000000)
178. feature "BST2" (0.000000)
179. feature "NKX6-1" (0.000000)
180. feature "LHX2" (0.000000)
181. feature "THSD7B" (0.000000)
182. feature "VSIG1" (0.000000)
183. feature "RGS16" (0.000000)
184. feature "PRDM9" (0.000000)
185. feature "GADD45A" (0.000000)
186. feature "NAALAD2" (0.000000)
187. feature "SYT16" (0.000000)
188. feature "FAM111B" (0.000000)
189. feature "DCXR-DT" (0.000000)
190. feature "TRPC4" (0.000000)
191. feature "ZFP41" (0.000000)
192. feature "SERPINB11" (0.000000)
193. feature "WNT4" (0.000000)
194. feature "ZNF221" (0.000000)
195. feature "GEM" (0.000000)
196. feature "IL6R-AS1" (0.000000)
197. feature "CCN3" (0.000000)
198. feature "MIXL1" (0.000000)
199. feature "CACFD1" (0.000000)
200. feature "TEP1" (0.000000)
201. feature "TTC28" (0.000000)
202. feature "SEMA4B" (0.000000)
203. feature "MAG" (0.000000)
204. feature "SMIM5" (0.000000)
205. feature "ZBTB26" (0.000000)
206. feature "ZNF182" (0.000000)
207. feature "NTF3" (0.000000)
208. feature "IL2RG" (0.000000)
209. feature "LINC02701" (0.000000)
210. feature "ACTG1" (0.000000)
211. feature "ZNF775" (0.000000)
212. feature "SLC6A17" (0.000000)
213. feature "RPSAP41" (0.000000)
214. feature "TNIP3" (0.000000)
215. feature "ZWINT" (0.000000)
216. feature "RNF152" (0.000000)
217. feature "LINC01819" (0.000000)
218. feature "HEG1" (0.000000)
219. feature "RHOV" (0.000000)
220. feature "SIDT2" (0.000000)
221. feature "NOXA1" (0.000000)
222. feature "PCDHB6" (0.000000)
223. feature "USP27X" (0.000000)
224. feature "EFCAB6-DT" (0.000000)
225. feature "OVGP1" (0.000000)
226. feature "SLC27A1" (0.000000)
227. feature "CATSPER3" (0.000000)
228. feature "PRSS23" (0.000000)
229. feature "SPON2" (0.000000)
230. feature "KIF14" (0.000000)
231. feature "STEAP4" (0.000000)
232. feature "GFPT1" (0.000000)
233. feature "ARHGAP24" (0.000000)
234. feature "MUC7" (0.000000)
235. feature "RASSF10" (0.000000)
236. feature "DLK2" (0.000000)
237. feature "LUM" (0.000000)
238. feature "ZNF75D" (0.000000)
239. feature "ZNF185" (0.000000)
240. feature "PI3" (0.000000)
241. feature "CYP2R1" (0.000000)
242. feature "FRAT1" (0.000000)
243. feature "ASPM" (0.000000)
244. feature "SLC1A4" (0.000000)
245. feature "GPER1" (0.000000)
246. feature "XK" (0.000000)
247. feature "FNDC5" (0.000000)
248. feature "CNR1" (0.000000)
249. feature "HBP1" (0.000000)
250. feature "PFKFB4" (0.000000)
251. feature "BMP6" (0.000000)
252. feature "ANXA9" (0.000000)
253. feature "CLIC3" (0.000000)
254. feature "CES4A" (0.000000)
255. feature "ACKR2" (0.000000)
256. feature "KCTD8" (0.000000)
257. feature "ANXA10" (0.000000)
258. feature "MYH14" (0.000000)
259. feature "CPLANE2" (0.000000)
260. feature "CD14" (0.000000)
261. feature "CLCN5" (0.000000)
262. feature "MALAT1" (0.000000)
263. feature "SPATA12" (0.000000)
264. feature "GGT1" (0.000000)
265. feature "IL1RL1" (0.000000)
266. feature "SDK1" (0.000000)
267. feature "SLCO4C1" (0.000000)
268. feature "ITGB2" (0.000000)
269. feature "LIPA" (0.000000)
270. feature "SP6" (0.000000)
271. feature "ALDH1A3" (0.000000)
272. feature "DGKQ" (0.000000)
273. feature "DMRTA1" (0.000000)
274. feature "CD86" (0.000000)
275. feature "P2RY6" (0.000000)
276. feature "DAPK2" (0.000000)
277. feature "LURAP1" (0.000000)
278. feature "KCNN3" (0.000000)
279. feature "ZSCAN20" (0.000000)
280. feature "TNFRSF1B" (0.000000)
281. feature "ABCA3" (0.000000)
282. feature "LINC00858" (0.000000)
283. feature "GAS7" (0.000000)
284. feature "BCL6" (0.000000)
285. feature "PRSS21" (0.000000)
286. feature "KRT18" (0.000000)
287. feature "IL12RB2" (0.000000)
288. feature "TAGLN" (0.000000)
289. feature "DDIT3" (0.000000)
290. feature "RAPGEFL1" (0.000000)
291. feature "TSPAN8" (0.000000)
292. feature "AZIN2" (0.000000)
293. feature "ASB2" (0.000000)
294. feature "SPOCK2" (0.000000)
295. feature "CLMN" (0.000000)
296. feature "FAM218A" (0.000000)
297. feature "P4HTM" (0.000000)
298. feature "CD99L2" (0.000000)
299. feature "FGF12" (0.000000)
300. feature "GREM2" (0.000000)
301. feature "TMEM74B" (0.000000)
302. feature "TUBB4A" (0.000000)
303. feature "C10orf143" (0.000000)
304. feature "ZNF805" (0.000000)
305. feature "ZNF169" (0.000000)
306. feature "JUNB" (0.000000)
307. feature "CMPK2" (0.000000)
308. feature "AXIN2" (0.000000)
309. feature "MYC" (0.000000)
310. feature "NWD1" (0.000000)
311. feature "ZNF665" (0.000000)
312. feature "MYH3" (0.000000)
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2812. feature "B3GNT3" (0.000000)
2813. feature "FLNC" (0.000000)
2814. feature "MET" (0.000000)
2815. feature "OLFML3" (0.000000)
2816. feature "FCER1G" (0.000000)
2817. feature "ITGA2" (0.000000)
2818. feature "LINC00921" (0.000000)
2819. feature "OXTR" (0.000000)
2820. feature "COX6B2" (0.000000)
2821. feature "TM4SF1" (0.000000)
2822. feature "SCIRT" (0.000000)
2823. feature "PTPRN" (0.000000)
2824. feature "PLAC8L1" (0.000000)
2825. feature "IGFL1" (0.000000)
2826. feature "PEAR1" (0.000000)
2827. feature "MT-CYB" (0.000000)
2828. feature "BLCAP" (0.000000)
2829. feature "NAV1" (0.000000)
2830. feature "ZSCAN30" (0.000000)
2831. feature "RAPGEF3" (0.000000)
2832. feature "ADGRG2" (0.000000)
2833. feature "C19orf48" (0.000000)
2834. feature "KIF6" (0.000000)
2835. feature "TNFRSF10D" (0.000000)
2836. feature "VIPR1-AS1" (0.000000)
2837. feature "FGFR1" (0.000000)
2838. feature "ZBTB10" (0.000000)
2839. feature "PATL2" (0.000000)
2840. feature "LIMS2" (0.000000)
2841. feature "MIR34AHG" (0.000000)
2842. feature "NPTN-IT1" (0.000000)
2843. feature "C11orf87" (0.000000)
2844. feature "ZNF214" (0.000000)
2845. feature "PLOD2" (0.000000)
2846. feature "TSSK4" (0.000000)
2847. feature "APCDD1L" (0.000000)
2848. feature "KCNK2" (0.000000)
2849. feature "TLR4" (0.000000)
2850. feature "WARS1" (0.000000)
2851. feature "FAM47E" (0.000000)
2852. feature "EFNB3" (0.000000)
2853. feature "HMCN1" (0.000000)
2854. feature "FTLP12" (0.000000)
2855. feature "NINJ2" (0.000000)
2856. feature "E2F1" (0.000000)
2857. feature "MYH9" (0.000000)
2858. feature "COL9A2" (0.000000)
2859. feature "LINC01203" (0.000000)
2860. feature "FABP5" (0.000000)
2861. feature "VAMP5" (0.000000)
2862. feature "CEP55" (0.000000)
2863. feature "COL8A2" (0.000000)
2864. feature "CELF5" (0.000000)
2865. feature "WNT5A" (0.000000)
2866. feature "XYLT1" (0.000000)
2867. feature "LINC00242" (0.000000)
2868. feature "FAM229A" (0.000000)
2869. feature "ZPLD2P" (0.000000)
2870. feature "NOTUM" (0.000000)
2871. feature "MDGA1" (0.000000)
2872. feature "NNMT" (0.000000)
2873. feature "SEMA4C" (0.000000)
2874. feature "HSPA12B" (0.000000)
2875. feature "PLAAT1" (0.000000)
2876. feature "OR1F1" (0.000000)
2877. feature "PPP1R21-DT" (0.000000)
2878. feature "FAM186B" (0.000000)
2879. feature "SELENOP" (0.000000)
2880. feature "HIPK1-AS1" (0.000000)
2881. feature "MT-ND4" (0.000000)
2882. feature "TPRG1-AS1" (0.000000)
2883. feature "LRATD1" (0.000000)
2884. feature "ASPA" (0.000000)
2885. feature "SLC25A19" (0.000000)
2886. feature "LINC00222" (0.000000)
2887. feature "RASL11A" (0.000000)
2888. feature "MATN3" (0.000000)
2889. feature "FSCN1" (0.000000)
2890. feature "TMEM51-AS1" (0.000000)
2891. feature "CPT1B" (0.000000)
2892. feature "ZNF574" (0.000000)
2893. feature "SLC1A5" (0.000000)
2894. feature "CRYM-AS1" (0.000000)
2895. feature "FAM193B-DT" (0.000000)
2896. feature "MMP28" (0.000000)
2897. feature "CCNT2-AS1" (0.000000)
2898. feature "KLHL29" (0.000000)
2899. feature "ACTN1" (0.000000)
2900. feature "ARHGAP31-AS1" (0.000000)
2901. feature "FAM111A" (0.000000)
2902. feature "LINC02009" (0.000000)
2903. feature "SEMA6C" (0.000000)
2904. feature "ZNF540" (0.000000)
2905. feature "MFRP" (0.000000)
2906. feature "STK33" (0.000000)
2907. feature "DLSTP1" (0.000000)
2908. feature "DCLK1" (0.000000)
2909. feature "OTULIN-DT" (0.000000)
2910. feature "PLPP6" (0.000000)
2911. feature "MT-CO2" (0.000000)
2912. feature "XDH" (0.000000)
2913. feature "SYT17" (0.000000)
2914. feature "SAMD13" (0.000000)
2915. feature "FZD4-DT" (0.000000)
2916. feature "CNTN1" (0.000000)
2917. feature "CDKN2B-AS1" (0.000000)
2918. feature "IRAK2" (0.000000)
2919. feature "APLP1" (0.000000)
2920. feature "AKR7A3" (0.000000)
2921. feature "RAMP2" (0.000000)
2922. feature "EPCAM-DT" (0.000000)
2923. feature "TTK" (0.000000)
2924. feature "SH3BP5L" (0.000000)
2925. feature "DCBLD2" (0.000000)
2926. feature "LINC00243" (0.000000)
2927. feature "MAP4K1" (0.000000)
2928. feature "PALM2AKAP2" (0.000000)
2929. feature "GUCY1B1" (0.000000)
2930. feature "ZKSCAN3" (0.000000)
2931. feature "ZCCHC2" (0.000000)
2932. feature "QRSL1P3" (0.000000)
2933. feature "SETBP1-DT" (0.000000)
2934. feature "HSP90B1" (0.000000)
2935. feature "SEC61A1" (0.000000)
2936. feature "RPL7AP10" (0.000000)
2937. feature "GPR158" (0.000000)
2938. feature "KLRA1P" (0.000000)
2939. feature "ZNF711" (0.000000)
2940. feature "RIBC1" (0.000000)
2941. feature "ALOX15" (0.000000)
2942. feature "LINC00571" (0.000000)
2943. feature "ZNF398" (0.000000)
2944. feature "KIAA0040" (0.000000)
2945. feature "LINC00518" (0.000000)
2946. feature "TAS2R2P" (0.000000)
2947. feature "HPDL" (0.000000)
2948. feature "IMPDH1P10" (0.000000)
2949. feature "TCP10L" (0.000000)
2950. feature "PFKL" (0.000000)
2951. feature "LINC01833" (0.000000)
2952. feature "STK31" (0.000000)
2953. feature "BCAR3" (0.000000)
2954. feature "PAX7" (0.000000)
2955. feature "LINC01270" (0.000000)
2956. feature "CDCA3" (0.000000)
2957. feature "YPEL5" (0.000000)
2958. feature "RIMS3" (0.000000)
2959. feature "LINC01752" (0.000000)
2960. feature "SLAMF8" (0.000000)
2961. feature "TUBA1B" (0.000000)
2962. feature "PCED1B-AS1" (0.000000)
2963. feature "FABP3" (0.000000)
2964. feature "PIGZ" (0.000000)
2965. feature "BCRP9" (0.000000)
2966. feature "TDRKH-AS1" (0.000000)
2967. feature "ZNF473" (0.000000)
2968. feature "PPM1L" (0.000000)
2969. feature "TMEM249" (0.000000)
2970. feature "ADCY10" (0.000000)
2971. feature "LINC01956" (0.000000)
2972. feature "PLEKHA7" (0.000000)
2973. feature "CYYR1" (0.000000)
2974. feature "SH2D2A" (0.000000)
2975. feature "SLC1A3" (0.000000)
2976. feature "FBXO43" (0.000000)
2977. feature "ZC3H12A" (0.000000)
2978. feature "TACSTD2" (0.000000)
2979. feature "KLHL22" (0.000000)
2980. feature "HES1" (0.000000)
2981. feature "SLIT1" (0.000000)
2982. feature "CCK" (0.000000)
2983. feature "TP53I11" (0.000000)
2984. feature "HLA-DOB" (0.000000)
2985. feature "TRPV4" (0.000000)
2986. feature "CAMK1D" (0.000000)
2987. feature "CRABP2" (0.000000)
2988. feature "LYPD5" (0.000000)
2989. feature "ZNF517" (0.000000)
2990. feature "HLX" (0.000000)
2991. feature "SERPINF2" (0.000000)
2992. feature "FTCDNL1" (0.000000)
2993. feature "KRT80" (0.000000)
2994. feature "MSC" (0.000000)
2995. feature "LINC02085" (0.000000)
2996. feature "CCDC96" (0.000000)
2997. feature "RNASE7" (0.000000)
2998. feature "RHBG" (0.000000)
2999. feature "NQO1" (0.000000)
3000. feature "DDIT4" (0.000000)
¶
4.2.4) KNN
best_knn_SS_HCC1806=train_and_evaluate_knn(X_train_SS_HCC, X_test_SS_HCC, y_train_SS_HCC, y_test_SS_HCC)
Best hyperparameters: {'n_neighbors': 3}
Accuracy: 0.7837837837837838
F1: 0.7894736842105263
Classification Report:
precision recall f1-score support
0.0 0.64 1.00 0.78 14
1.0 1.00 0.65 0.79 23
accuracy 0.78 37
macro avg 0.82 0.83 0.78 37
weighted avg 0.86 0.78 0.79 37
¶
4.2.5) Logistic Regression
best_logreg_SS_HCC1806=train_and_evaluate_logreg(X_train_SS_HCC, X_test_SS_HCC, y_train_SS_HCC, y_test_SS_HCC)
Best hyperparameters: {'C': 0.002, 'penalty': 'l2'}
Accuracy: 1.0
F1: 1.0
Classification Report:
precision recall f1-score support
0.0 1.00 1.00 1.00 14
1.0 1.00 1.00 1.00 23
accuracy 1.00 37
macro avg 1.00 1.00 1.00 37
weighted avg 1.00 1.00 1.00 37
# try pca method to reduce number of features and use log log regression
X_train_pca_SS_HCC, X_test_pca_SS_HCC, y_train_SS_HCC, y_test_SS_HCC,scaler_SS_HCC,pca_SS_HCC = prepare_data_pca(df_trans_SS_HCC)
X_train_pca_SS_HCC.shape
(145, 89)
model_logreg_hcc_SMART_ver2_SS_HCC=train_and_evaluate_logreg(X_train_pca_SS_HCC, X_test_pca_SS_HCC, y_train_SS_HCC, y_test_SS_HCC)
Best hyperparameters: {'C': 0.002, 'penalty': 'l2'}
Accuracy: 1.0
F1: 1.0
Classification Report:
precision recall f1-score support
0.0 1.00 1.00 1.00 14
1.0 1.00 1.00 1.00 23
accuracy 1.00 37
macro avg 1.00 1.00 1.00 37
weighted avg 1.00 1.00 1.00 37
¶
4.2.6) CatBoost
best_catboost_SS_HCC1806=train_and_evaluate_catboost(X_train_SS_HCC, X_test_SS_HCC, y_train_SS_HCC, y_test_SS_HCC)
Best hyperparameters: {'logging_level': 'Silent'}
Accuracy: 1.0
F1: 1.0
Classification Report:
precision recall f1-score support
0.0 1.00 1.00 1.00 14
1.0 1.00 1.00 1.00 23
accuracy 1.00 37
macro avg 1.00 1.00 1.00 37
weighted avg 1.00 1.00 1.00 37
new_columns_names_SS_HCC = []
for i in range(1,3001):
name_new_SS_HCC = f'f{i}'
new_columns_names_SS_HCC.append(name_new_SS_HCC)
X_test_pd_SS_HCC=pd.DataFrame(X_test_SS_HCC, columns=new_columns_names_SS_HCC)
X_train_pd_SS_HCC=pd.DataFrame(X_train_SS_HCC, columns=new_columns_names_SS_HCC)
model_catboost_hcc_SMART_ver2_SS_HCC,cols_cut_SS_HCC=train_and_evaluate_catboost2(X_train_pd_SS_HCC, X_test_pd_SS_HCC, y_train_SS_HCC, y_test_SS_HCC)
1174
Best hyperparameters: {'l2_leaf_reg': 3, 'logging_level': 'Silent', 'n_estimators': 500}
Accuracy: 1.0
F1: 1.0
Classification Report:
precision recall f1-score support
0.0 1.00 1.00 1.00 14
1.0 1.00 1.00 1.00 23
accuracy 1.00 37
macro avg 1.00 1.00 1.00 37
weighted avg 1.00 1.00 1.00 37
¶
4.3) PREDICTORS DROPSEQ MCF7
We apply the same steps of the dataset before (see 4.1) to the DropSeq MCF7 dataset
#load the DropSeq MCF7 dataset
df_DS_MCF7_norm_filt = pd.read_csv("DropSeq\MCF7_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)
df_DS_MCF7_norm_filtT = df_DS_MCF7_norm_filt.T
# Add the target column
df_DS_MCF7_norm_filtT['target'] = np.where(df_DS_MCF7_norm_filtT.index.str.contains('Hypo'), 1, 0)
#i want to split the data into training and testing data, with a balance of 70% training and 30% testing
X_df_DS_MCF7 = df_DS_MCF7_norm_filtT.drop('target', axis=1)
y_df_DS_MCF7 = df_DS_MCF7_norm_filtT['target']
X_train_df_DS_MCF7, X_test_df_DS_MCF7, y_train_df_DS_MCF7, y_test_df_DS_MCF7 = train_test_split(X_df_DS_MCF7, y_df_DS_MCF7, test_size=0.2, random_state=42)
df_basic_DS_MCF=df_DS_MCF7_norm_filt
df_trans_DS_MCF=df_basic_DS_MCF.T
df_trans_DS_MCF['TARGET'] = df_trans_DS_MCF.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_DS_MCF.loc[df_trans_DS_MCF.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_DS_MCF.loc[df_trans_DS_MCF.index.str.contains('Hypo'), 'TARGET'] = 1
X_train_DS_MCF, X_test_DS_MCF, y_train_DS_MCF, y_test_DS_MCF,scaler_DS_MCF = prepare_data(df_trans_DS_MCF)
¶
4.3.1) SVM
We used same approach used for SmartSeq MCF7, see 4.1.1.
# build SVM on DropSeq MCF7
# Define the SVM classifier
svm = SVC(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'kernel': ['linear', 'rbf']}
grid_search_svm = GridSearchCV(svm, param_grid, cv=2)
# Define the leave one out cross validation
#loo = LeaveOneOut()
# Perform the n
# ested cross validation
scores_svm = cross_val_score(grid_search_svm, X_train_df_DS_MCF7, y_train_df_DS_MCF7, cv=2)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of 2 fold cross validation: ", np.mean(scores_svm))
Mean accuracy of 2 fold cross validation: 0.9778034682080925
# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search_svm.fit(X_train_df_DS_MCF7, y_train_df_DS_MCF7)
print("Best kernel: ", grid_search_svm.best_params_)
print("Best accuracy: ", grid_search_svm.best_score_)
print("Best model: ", grid_search_svm.best_estimator_)
best_svm_DS_MCF7 = grid_search_svm.best_estimator_
# Test the model on the test dataset
print("Test accuracy: ", best_svm_DS_MCF7.score(X_test_df_DS_MCF7, y_test_df_DS_MCF7))
Best kernel: {'kernel': 'rbf'}
Best accuracy: 0.9778034682080925
Best model: SVC(random_state=42)
Test accuracy: 0.9798890429958391
¶
4.3.2) Random Forest
Here we build the random forest classifier for the DropSeq MCF7 dataset.
We followed also here the same steps as before but with a different approach in the cross validation.
Since the dataset is very big we decided to substitute the LeaveOneOut validation with a normal k-fold validation.
Another slight difference can be seen in the number of parameters, where the search was done for 50, 60, 100 estimators.
Also the max depth was sligtly augmented in the search to 30, 50, 60.
We point out also the fact that augmenting the parameters and the max depth didn't lead our model to overfit as the mean accuracy in the cross validation is high.
The overall performance of the model in the test set is 96,50%.
Something interesting can be seen in the feature ranking list, we noticed that the gene MT_RNR2 was the second most important, and this confiermed us that the intuition we had to look at the genes that seemed to have the highest difference in mean between the two samples was indeed a good way to explore the data.
#i build the random forest classifier for the DropSeq MCF7 dataset
# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [50, 60, 100], 'max_depth': [30, 50, 60]}
grid_search = GridSearchCV(rf, param_grid, cv=5)
outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)
# Perform the cross validation
scores = cross_val_score(grid_search, X_train_df_DS_MCF7, y_train_df_DS_MCF7, cv=outer_cv)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))
# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_DS_MCF7, y_train_df_DS_MCF7)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)
#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_DS_MCF7, y_test_df_DS_MCF7))
# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_DS_MCF7.shape[1]):
print("%d. feature %s (%f)" % (f + 1, X_train_df_DS_MCF7.columns[indices[f]], importances[indices[f]]))
best_rf_DS_HCC1806 = grid_search.best_estimator_
Mean accuracy of nested cross validation: 0.9651871815047226
Best hyperparameters: {'max_depth': 50, 'n_estimators': 100}
Best accuracy: 0.9656491183051349
Best model: RandomForestClassifier(max_depth=50, random_state=42)
Test accuracy: 0.9650123304562269
Feature ranking:
1. feature "TFF1" (0.038533)
2. feature "MT-RNR2" (0.033664)
3. feature "KRT19" (0.030886)
4. feature "PGK1" (0.027427)
5. feature "GAPDH" (0.024421)
6. feature "TMSB10" (0.023137)
7. feature "RPL13" (0.021265)
8. feature "TPI1" (0.018440)
9. feature "RPS14" (0.018437)
10. feature "RPS19" (0.018002)
11. feature "MT-RNR1" (0.017737)
12. feature "LGALS1" (0.016023)
13. feature "RPL15" (0.015649)
14. feature "ENO1" (0.012904)
15. feature "PKM" (0.012042)
16. feature "RPL35" (0.011413)
17. feature "RPL12" (0.011380)
18. feature "RPL34" (0.011354)
19. feature "RPL36" (0.009996)
20. feature "MALAT1" (0.009275)
21. feature "TMEM258" (0.009189)
22. feature "RPS15A" (0.009098)
23. feature "RPL30" (0.009027)
24. feature "TFF3" (0.008791)
25. feature "RPLP1" (0.008760)
26. feature "HES1" (0.008350)
27. feature "MT-ND4" (0.008284)
28. feature "RPL39" (0.008220)
29. feature "RPS5" (0.008115)
30. feature "BCYRN1" (0.008098)
31. feature "ROMO1" (0.007435)
32. feature "RPL35A" (0.006409)
33. feature "RPLP2" (0.006336)
34. feature "H4C3" (0.006299)
35. feature "S100A10" (0.006285)
36. feature "RPS15" (0.006232)
37. feature "SNRPD2" (0.006232)
38. feature "SERF2" (0.006149)
39. feature "RPL23" (0.006011)
40. feature "DSP" (0.005712)
41. feature "HSPB1" (0.005571)
42. feature "RPS16" (0.005532)
43. feature "RPS12" (0.005227)
44. feature "TOB1" (0.004840)
45. feature "S100A11" (0.004817)
46. feature "KRT18" (0.004796)
47. feature "RPS27" (0.004792)
48. feature "ELOB" (0.004686)
49. feature "COX7A2" (0.004679)
50. feature "KRT8" (0.004648)
51. feature "SET" (0.004566)
52. feature "FTL" (0.004459)
53. feature "UBA52" (0.004420)
54. feature "ACTB" (0.004247)
55. feature "UQCRQ" (0.004241)
56. feature "MT-ND3" (0.004118)
57. feature "COX7C" (0.004041)
58. feature "ATP5MK" (0.003989)
59. feature "MT-CYB" (0.003911)
60. feature "MT-CO2" (0.003723)
61. feature "MT-CO3" (0.003549)
62. feature "RPL37A" (0.003418)
63. feature "NDUFB2" (0.003290)
64. feature "TMEM64" (0.003227)
65. feature "SOX4" (0.002900)
66. feature "MT-ATP6" (0.002865)
67. feature "DSCAM-AS1" (0.002821)
68. feature "FGF23" (0.002609)
69. feature "ATP5ME" (0.002569)
70. feature "RPL37" (0.002447)
71. feature "RPS28" (0.002436)
72. feature "PARD6B" (0.002406)
73. feature "MT2A" (0.002355)
74. feature "TRIM37" (0.002043)
75. feature "BCAS3" (0.001888)
76. feature "MT-ND4L" (0.001868)
77. feature "RPL11" (0.001852)
78. feature "RPL29" (0.001851)
79. feature "PKIB" (0.001829)
80. feature "PRRC2C" (0.001771)
81. feature "MT-ND5" (0.001688)
82. feature "TPD52L1" (0.001650)
83. feature "NEDD8" (0.001644)
84. feature "C4orf48" (0.001631)
85. feature "NCL" (0.001606)
86. feature "H2AC12" (0.001577)
87. feature "PFDN4" (0.001566)
88. feature "RPL28" (0.001526)
89. feature "CSDE1" (0.001513)
90. feature "H2AC20" (0.001491)
91. feature "EIF4G2" (0.001475)
92. feature "HSPD1" (0.001469)
93. feature "TPM4" (0.001435)
94. feature "GOLGA4" (0.001434)
95. feature "DNAJA1" (0.001362)
96. feature "NEAT1" (0.001342)
97. feature "IGFBP5" (0.001327)
98. feature "ANP32B" (0.001318)
99. feature "H1-4" (0.001279)
100. feature "NCOA3" (0.001248)
101. feature "ATP1A1" (0.001226)
102. feature "YWHAZ" (0.001219)
103. feature "YWHAB" (0.001216)
104. feature "HDGF" (0.001206)
105. feature "GPM6A" (0.001179)
106. feature "MT-ND2" (0.001158)
107. feature "MT-CO1" (0.001157)
108. feature "CAPZA1" (0.001155)
109. feature "VMP1" (0.001140)
110. feature "CALM2" (0.001128)
111. feature "PABPC1" (0.001122)
112. feature "TPM3" (0.001113)
113. feature "CKS2" (0.001111)
114. feature "PSMA7" (0.001101)
115. feature "HNRNPU" (0.001080)
116. feature "CBX3" (0.001075)
117. feature "RPS6KB1" (0.001059)
118. feature "HNRNPA2B1" (0.001057)
119. feature "H1-2" (0.001026)
120. feature "H1-5" (0.001009)
121. feature "RAD23B" (0.001005)
122. feature "MT-ND6" (0.000966)
123. feature "PTP4A2" (0.000947)
124. feature "DEK" (0.000938)
125. feature "BPTF" (0.000936)
126. feature "AREG" (0.000916)
127. feature "GATA3" (0.000907)
128. feature "PAK2" (0.000906)
129. feature "HNRNPAB" (0.000906)
130. feature "LARP4B" (0.000897)
131. feature "HSPH1" (0.000892)
132. feature "PDAP1" (0.000884)
133. feature "H1-3" (0.000874)
134. feature "MKI67" (0.000871)
135. feature "EIF3A" (0.000854)
136. feature "RHOBTB3" (0.000848)
137. feature "H2AC11" (0.000846)
138. feature "EIF5B" (0.000810)
139. feature "PPM1G" (0.000807)
140. feature "ARF3" (0.000802)
141. feature "ARPP19" (0.000798)
142. feature "STRBP" (0.000788)
143. feature "SLC9A3R1" (0.000769)
144. feature "PTPN11" (0.000767)
145. feature "ATAD2" (0.000750)
146. feature "NSD1" (0.000745)
147. feature "C7orf50" (0.000737)
148. feature "POLR2I" (0.000736)
149. feature "METAP2" (0.000720)
150. feature "RPS27L" (0.000717)
151. feature "NBN" (0.000711)
152. feature "PAPOLA" (0.000703)
153. feature "COPS9" (0.000700)
154. feature "H19" (0.000698)
155. feature "BAZ1B" (0.000692)
156. feature "HIF1A" (0.000692)
157. feature "CENPF" (0.000688)
158. feature "TOP2A" (0.000680)
159. feature "SMC4" (0.000669)
160. feature "XBP1" (0.000666)
161. feature "GNL3L" (0.000663)
162. feature "SLC25A24" (0.000662)
163. feature "LXN" (0.000658)
164. feature "MT-ND1" (0.000651)
165. feature "SPATS2L" (0.000648)
166. feature "POLR2J" (0.000641)
167. feature "AP2S1" (0.000637)
168. feature "GSPT1" (0.000630)
169. feature "MAN1A2" (0.000630)
170. feature "S100P" (0.000626)
171. feature "EIF3J" (0.000601)
172. feature "RBM25" (0.000592)
173. feature "SVIP" (0.000592)
174. feature "TPM1" (0.000590)
175. feature "C9orf78" (0.000588)
176. feature "TIMM8B" (0.000586)
177. feature "CDV3" (0.000577)
178. feature "NDUFS6" (0.000574)
179. feature "NCALD" (0.000574)
180. feature "H1-0" (0.000573)
181. feature "HMGB2" (0.000569)
182. feature "PRRG3" (0.000560)
183. feature "ZNF217" (0.000557)
184. feature "CAST" (0.000557)
185. feature "WDR43" (0.000553)
186. feature "HNRNPD" (0.000549)
187. feature "ANKRD11" (0.000543)
188. feature "AURKA" (0.000534)
189. feature "ZNF121" (0.000525)
190. feature "ZC3H15" (0.000523)
191. feature "BASP1" (0.000523)
192. feature "GTF3A" (0.000514)
193. feature "RAD21" (0.000513)
194. feature "TBCA" (0.000510)
195. feature "C8orf33" (0.000507)
196. feature "TPX2" (0.000507)
197. feature "PRPF40A" (0.000507)
198. feature "ENAH" (0.000506)
199. feature "AP1G1" (0.000498)
200. feature "MGRN1" (0.000493)
201. feature "G6PD" (0.000489)
202. feature "SYTL2" (0.000488)
203. feature "ATRX" (0.000486)
204. feature "TARS1" (0.000481)
205. feature "METRN" (0.000479)
206. feature "HNRNPUL1" (0.000476)
207. feature "NONO" (0.000475)
208. feature "NPM1P40" (0.000474)
209. feature "RALBP1" (0.000469)
210. feature "LARP1" (0.000465)
211. feature "JAK1" (0.000463)
212. feature "KIF5B" (0.000460)
213. feature "USP8" (0.000460)
214. feature "BRIP1" (0.000456)
215. feature "UBE2K" (0.000456)
216. feature "DYNLL2" (0.000453)
217. feature "CSK" (0.000453)
218. feature "GADD45GIP1" (0.000452)
219. feature "CCNI" (0.000451)
220. feature "SYNE2" (0.000448)
221. feature "NCOR2" (0.000446)
222. feature "MT-TQ" (0.000444)
223. feature "HTATSF1" (0.000444)
224. feature "KPNA2" (0.000444)
225. feature "CACNA1A" (0.000443)
226. feature "KRT10" (0.000443)
227. feature "NRIP1" (0.000441)
228. feature "CLIP1" (0.000439)
229. feature "GPATCH4" (0.000439)
230. feature "FAM177A1" (0.000437)
231. feature "SLTM" (0.000435)
232. feature "MRPS34" (0.000435)
233. feature "CDKN3" (0.000432)
234. feature "MYH9" (0.000432)
235. feature "FAM120A" (0.000430)
236. feature "PSMC5" (0.000429)
237. feature "ANP32E" (0.000425)
238. feature "UQCC2" (0.000423)
239. feature "PSMD14" (0.000423)
240. feature "LEO1" (0.000422)
241. feature "PPIG" (0.000421)
242. feature "UGDH" (0.000417)
243. feature "ROCK1" (0.000416)
244. feature "MED13" (0.000415)
245. feature "PHACTR2" (0.000412)
246. feature "RSRC2" (0.000412)
247. feature "C6orf62" (0.000411)
248. feature "DNAJC2" (0.000408)
249. feature "SNX27" (0.000407)
250. feature "DNMT1" (0.000406)
251. feature "PTBP3" (0.000406)
252. feature "MT-ATP8" (0.000402)
253. feature "PMEPA1" (0.000402)
254. feature "ANKRD52" (0.000401)
255. feature "SSRP1" (0.000398)
256. feature "URI1" (0.000396)
257. feature "DKC1" (0.000395)
258. feature "BDP1" (0.000391)
259. feature "NFIC" (0.000390)
260. feature "BAP1" (0.000390)
261. feature "CAV1" (0.000390)
262. feature "TRIP12" (0.000389)
263. feature "SPDL1" (0.000388)
264. feature "R3HDM2" (0.000386)
265. feature "CLTB" (0.000385)
266. feature "EIF2AK1" (0.000384)
267. feature "S100A16" (0.000379)
268. feature "RNPS1" (0.000379)
269. feature "CMSS1" (0.000376)
270. feature "CENPU" (0.000375)
271. feature "SUDS3" (0.000375)
272. feature "VTI1B" (0.000375)
273. feature "DDX21" (0.000375)
274. feature "HCFC1" (0.000373)
275. feature "PLBD2" (0.000370)
276. feature "KCNQ1OT1" (0.000370)
277. feature "PLEC" (0.000368)
278. feature "NIPBL" (0.000368)
279. feature "ITPRID2" (0.000367)
280. feature "MGP" (0.000366)
281. feature "SENP6" (0.000363)
282. feature "TRIM44" (0.000363)
283. feature "SPRY1" (0.000362)
284. feature "SUMO3" (0.000362)
285. feature "UQCC3" (0.000361)
286. feature "ZNF302" (0.000361)
287. feature "HNRNPH3" (0.000360)
288. feature "KPNA4" (0.000360)
289. feature "CENPB" (0.000359)
290. feature "STARD10" (0.000358)
291. feature "ZMYND11" (0.000358)
292. feature "CNBP" (0.000358)
293. feature "TAOK3" (0.000357)
294. feature "SEPTIN11" (0.000355)
295. feature "FBP1" (0.000355)
296. feature "ZC3H13" (0.000354)
297. feature "ATMIN" (0.000354)
298. feature "MYO5C" (0.000353)
299. feature "USP1" (0.000353)
300. feature "REST" (0.000352)
301. feature "ILF3" (0.000351)
302. feature "CDK2AP1" (0.000347)
303. feature "RTF1" (0.000346)
304. feature "ARID4B" (0.000346)
305. feature "SUPT6H" (0.000345)
306. feature "FAM102A" (0.000341)
307. feature "CDC37" (0.000340)
308. feature "ROCK2" (0.000340)
309. feature "HSPA4" (0.000340)
310. feature "DNAJC21" (0.000339)
311. feature "RAB13" (0.000338)
312. feature "ZBTB20" (0.000337)
313. feature "PHF3" (0.000337)
314. feature "PPP2R5E" (0.000337)
315. feature "GSE1" (0.000336)
316. feature "TSPYL1" (0.000335)
317. feature "ZFP36L1" (0.000332)
318. feature "MT-TS1" (0.000331)
319. feature "SLK" (0.000330)
320. feature "PRKACA" (0.000326)
321. feature "PHLDA2" (0.000325)
322. feature "SPAG9" (0.000321)
323. feature "PRMT2" (0.000321)
324. feature "STARD7" (0.000320)
325. feature "NDUFAF8" (0.000320)
326. feature "CEP55" (0.000319)
327. feature "EEA1" (0.000319)
328. feature "WSB2" (0.000319)
329. feature "BOD1L1" (0.000317)
330. feature "CDC42BPB" (0.000313)
331. feature "RRP15" (0.000311)
332. feature "GCC2" (0.000311)
333. feature "MIR663AHG" (0.000310)
334. feature "RBBP6" (0.000309)
335. feature "TNRC6A" (0.000307)
336. feature "CAMSAP2" (0.000304)
337. feature "HMGA1" (0.000303)
338. feature "RCC2" (0.000301)
339. feature "RABEP1" (0.000300)
340. feature "CHMP4B" (0.000299)
341. feature "EIF4E" (0.000299)
342. feature "NAA50" (0.000299)
343. feature "UGCG" (0.000299)
344. feature "R3HDM1" (0.000296)
345. feature "ZC3H18" (0.000296)
346. feature "MTCH1" (0.000296)
347. feature "FAM50A" (0.000296)
348. feature "INPP4B" (0.000295)
349. feature "SRGAP1" (0.000295)
350. feature "NSRP1" (0.000294)
351. feature "CENPE" (0.000294)
352. feature "UBE2Q1" (0.000292)
353. feature "ZNRF1" (0.000292)
354. feature "BCCIP" (0.000291)
355. feature "C5orf63" (0.000291)
356. feature "USP32" (0.000291)
357. feature "MDM2" (0.000289)
358. feature "IRAK1" (0.000289)
359. feature "BRD4" (0.000288)
360. feature "GARS1" (0.000288)
361. feature "TASOR2" (0.000288)
362. feature "TAF13" (0.000288)
363. feature "H2BC4" (0.000287)
364. feature "KPNA1" (0.000286)
365. feature "GATAD2A" (0.000285)
366. feature "USP7" (0.000285)
367. feature "FASN" (0.000285)
368. feature "DDX54" (0.000282)
369. feature "BRD7" (0.000282)
370. feature "CNNM2" (0.000281)
371. feature "MARCHF6" (0.000281)
372. feature "PREX1" (0.000279)
373. feature "CHD8" (0.000278)
374. feature "WWP1" (0.000276)
375. feature "MAP7" (0.000275)
376. feature "SOCS4" (0.000275)
377. feature "ZNF703" (0.000273)
378. feature "MRPL33" (0.000272)
379. feature "NAA10" (0.000272)
380. feature "APOOL" (0.000271)
381. feature "MAP3K13" (0.000271)
382. feature "ANKIB1" (0.000271)
383. feature "CASP8AP2" (0.000271)
384. feature "BTBD9" (0.000271)
385. feature "KIF1C" (0.000270)
386. feature "KCNJ2" (0.000269)
387. feature "KCNJ3" (0.000269)
388. feature "GNAQ" (0.000267)
389. feature "NEDD4L" (0.000266)
390. feature "NRBP1" (0.000265)
391. feature "SUPT5H" (0.000265)
392. feature "CNOT2" (0.000262)
393. feature "GPBP1" (0.000262)
394. feature "ARHGAP5" (0.000261)
395. feature "MDM4" (0.000260)
396. feature "TAOK1" (0.000260)
397. feature "RB1CC1" (0.000259)
398. feature "ZBTB7A" (0.000259)
399. feature "MAVS" (0.000258)
400. feature "SMG7" (0.000257)
401. feature "MTND1P23" (0.000257)
402. feature "MAGI3" (0.000256)
403. feature "PPP4R3A" (0.000256)
404. feature "FGD5-AS1" (0.000255)
405. feature "NORAD" (0.000255)
406. feature "NINJ1" (0.000253)
407. feature "KIF21A" (0.000253)
408. feature "MARK3" (0.000252)
409. feature "MAPKAPK2" (0.000250)
410. feature "WNK1" (0.000250)
411. feature "SOGA1" (0.000249)
412. feature "RBM23" (0.000249)
413. feature "REV3L" (0.000249)
414. feature "ARIH1" (0.000246)
415. feature "NET1" (0.000246)
416. feature "AP3D1" (0.000246)
417. feature "MYBL2" (0.000245)
418. feature "SMARCD2" (0.000245)
419. feature "GOLGA3" (0.000245)
420. feature "NIN" (0.000244)
421. feature "PPFIA1" (0.000242)
422. feature "RHOD" (0.000240)
423. feature "ISCU" (0.000240)
424. feature "BROX" (0.000240)
425. feature "CCDC186" (0.000239)
426. feature "RPS6KA6" (0.000238)
427. feature "LPP" (0.000238)
428. feature "ESCO1" (0.000238)
429. feature "SETD3" (0.000237)
430. feature "ATN1" (0.000237)
431. feature "RNF168" (0.000236)
432. feature "PROSER1" (0.000235)
433. feature "KMT5B" (0.000235)
434. feature "PRR34-AS1" (0.000231)
435. feature "RBBP8" (0.000231)
436. feature "SGO1" (0.000229)
437. feature "EGR1" (0.000229)
438. feature "SRM" (0.000229)
439. feature "DBF4" (0.000228)
440. feature "ZHX1" (0.000227)
441. feature "TIMELESS" (0.000226)
442. feature "SMIM15" (0.000225)
443. feature "ETF1" (0.000225)
444. feature "PRRC2A" (0.000224)
445. feature "SNHG9" (0.000223)
446. feature "PHF20L1" (0.000222)
447. feature "BTN3A2" (0.000220)
448. feature "SPIN1" (0.000220)
449. feature "SOS1" (0.000220)
450. feature "MAP3K2" (0.000219)
451. feature "IWS1" (0.000218)
452. feature "TLK2" (0.000218)
453. feature "ZMIZ1" (0.000217)
454. feature "SEPTIN9" (0.000217)
455. feature "MYBL1" (0.000217)
456. feature "NMT1" (0.000216)
457. feature "LYAR" (0.000215)
458. feature "DTYMK" (0.000215)
459. feature "BOLA3" (0.000215)
460. feature "CCP110" (0.000214)
461. feature "FNIP1" (0.000213)
462. feature "EFCAB14" (0.000213)
463. feature "CSKMT" (0.000213)
464. feature "EIF2AK2" (0.000212)
465. feature "KIF14" (0.000211)
466. feature "MYO10" (0.000210)
467. feature "PUS7" (0.000210)
468. feature "PRPF19" (0.000210)
469. feature "POLR2A" (0.000209)
470. feature "SETD2" (0.000209)
471. feature "GTF2F1" (0.000208)
472. feature "NAA15" (0.000208)
473. feature "BAZ2A" (0.000207)
474. feature "TAF3" (0.000207)
475. feature "CLSPN" (0.000206)
476. feature "ERBIN" (0.000205)
477. feature "KRT81" (0.000205)
478. feature "MRGBP" (0.000204)
479. feature "ARHGEF12" (0.000204)
480. feature "SECISBP2" (0.000204)
481. feature "MCRIP2" (0.000204)
482. feature "MAP4K4" (0.000203)
483. feature "HEPACAM" (0.000203)
484. feature "KRT80" (0.000202)
485. feature "ZNF316" (0.000202)
486. feature "ZC3H7A" (0.000201)
487. feature "LRRC59" (0.000201)
488. feature "KCTD3" (0.000201)
489. feature "BMS1" (0.000200)
490. feature "EBAG9" (0.000200)
491. feature "FARSA" (0.000199)
492. feature "IGFBP4" (0.000198)
493. feature "C2orf49" (0.000197)
494. feature "TIAL1" (0.000197)
495. feature "ZNF480" (0.000196)
496. feature "AHCYL1" (0.000195)
497. feature "EIF2B4" (0.000195)
498. feature "ETNK1" (0.000195)
499. feature "CTBP2" (0.000195)
500. feature "NSD2" (0.000194)
501. feature "CPSF2" (0.000194)
502. feature "ILRUN" (0.000194)
503. feature "PDS5A" (0.000194)
504. feature "NIBAN2" (0.000193)
505. feature "MARK2" (0.000193)
506. feature "SLC25A37" (0.000193)
507. feature "KCNK6" (0.000193)
508. feature "SART1" (0.000192)
509. feature "INCENP" (0.000192)
510. feature "CHP1" (0.000191)
511. feature "SMC6" (0.000191)
512. feature "FAM32A" (0.000191)
513. feature "NCKAP1" (0.000190)
514. feature "AP1M1" (0.000190)
515. feature "TNIP2" (0.000190)
516. feature "HIRIP3" (0.000189)
517. feature "GSR" (0.000189)
518. feature "KPNA3" (0.000189)
519. feature "TRAF3IP1" (0.000189)
520. feature "DAXX" (0.000188)
521. feature "SAPCD2" (0.000186)
522. feature "KMT2D" (0.000185)
523. feature "KLC1" (0.000185)
524. feature "PABPC4" (0.000185)
525. feature "MUC5B" (0.000185)
526. feature "CEP78" (0.000185)
527. feature "AFF4" (0.000184)
528. feature "RRP1B" (0.000184)
529. feature "FBXW5" (0.000184)
530. feature "ARID1B" (0.000183)
531. feature "CETN3" (0.000183)
532. feature "TUBD1" (0.000182)
533. feature "DCTN1" (0.000182)
534. feature "DCAF6" (0.000181)
535. feature "CTXN1" (0.000180)
536. feature "HIPK2" (0.000180)
537. feature "CFAP97" (0.000178)
538. feature "SMIM10L1" (0.000178)
539. feature "RPS19BP1" (0.000177)
540. feature "POLD2" (0.000177)
541. feature "YES1" (0.000177)
542. feature "DHX16" (0.000177)
543. feature "ANKRD17" (0.000176)
544. feature "ARFGEF1" (0.000176)
545. feature "AAMP" (0.000176)
546. feature "CBX6" (0.000176)
547. feature "HOOK1" (0.000175)
548. feature "ITPK1" (0.000175)
549. feature "GABPB2" (0.000175)
550. feature "MCL1" (0.000173)
551. feature "GALNT1" (0.000173)
552. feature "RAP1A" (0.000173)
553. feature "GDF15" (0.000173)
554. feature "OPA1" (0.000171)
555. feature "ZFC3H1" (0.000171)
556. feature "FAM199X" (0.000171)
557. feature "ALKBH5" (0.000171)
558. feature "DHTKD1" (0.000171)
559. feature "UTP18" (0.000171)
560. feature "FMR1" (0.000171)
561. feature "CA2" (0.000170)
562. feature "LRIF1" (0.000170)
563. feature "SCRIB" (0.000170)
564. feature "STX3" (0.000169)
565. feature "CDC42SE1" (0.000169)
566. feature "PRMT6" (0.000168)
567. feature "KIF3B" (0.000168)
568. feature "POGK" (0.000168)
569. feature "FAM111B" (0.000167)
570. feature "METRNL" (0.000167)
571. feature "PPM1K" (0.000167)
572. feature "DHX38" (0.000166)
573. feature "CAMK2N1" (0.000166)
574. feature "CNOT9" (0.000166)
575. feature "PLEKHB2" (0.000165)
576. feature "YAF2" (0.000165)
577. feature "NFKB1" (0.000165)
578. feature "MAPK1IP1L" (0.000164)
579. feature "OTULIN" (0.000164)
580. feature "CDK12" (0.000164)
581. feature "EIF4G3" (0.000164)
582. feature "LINC01304" (0.000163)
583. feature "ZNF146" (0.000163)
584. feature "SYCP2" (0.000163)
585. feature "CDKN1A" (0.000162)
586. feature "WIPI2" (0.000162)
587. feature "CCDC92" (0.000162)
588. feature "GOLGA5" (0.000162)
589. feature "SMC5" (0.000162)
590. feature "C1orf43" (0.000162)
591. feature "FBXW2" (0.000161)
592. feature "RBM14" (0.000161)
593. feature "NR2F6" (0.000161)
594. feature "PCYT1A" (0.000161)
595. feature "SPDEF" (0.000161)
596. feature "ATP6V1E1" (0.000160)
597. feature "LAD1" (0.000160)
598. feature "CSRP1" (0.000160)
599. feature "FKBP5" (0.000160)
600. feature "IQCE" (0.000159)
601. feature "PSME4" (0.000159)
602. feature "DNAJA2" (0.000158)
603. feature "PACS1" (0.000158)
604. feature "NOM1" (0.000157)
605. feature "ANKRD39" (0.000157)
606. feature "SCYL2" (0.000157)
607. feature "LINC01291" (0.000156)
608. feature "PCP4" (0.000156)
609. feature "CRNDE" (0.000156)
610. feature "BAG1" (0.000156)
611. feature "ELOA" (0.000156)
612. feature "OTUD7B" (0.000156)
613. feature "SENP7" (0.000156)
614. feature "NECAB1" (0.000156)
615. feature "EIF4EBP2" (0.000156)
616. feature "REV1" (0.000155)
617. feature "GBP1P1" (0.000155)
618. feature "TASOR" (0.000155)
619. feature "LRRFIP2" (0.000154)
620. feature "KHSRP" (0.000154)
621. feature "DROSHA" (0.000154)
622. feature "MT-TE" (0.000154)
623. feature "ZNF652" (0.000153)
624. feature "FBRS" (0.000153)
625. feature "SYTL4" (0.000153)
626. feature "RHNO1" (0.000153)
627. feature "UBE2J1" (0.000153)
628. feature "FUBP3" (0.000152)
629. feature "DNAJC5" (0.000152)
630. feature "ARHGAP12" (0.000152)
631. feature "OIP5-AS1" (0.000151)
632. feature "FRS2" (0.000151)
633. feature "PTGES2" (0.000150)
634. feature "MAFF" (0.000150)
635. feature "C5orf51" (0.000149)
636. feature "EPS8L2" (0.000149)
637. feature "DEDD" (0.000148)
638. feature "ANKRD26" (0.000148)
639. feature "EPB41L1" (0.000147)
640. feature "MIR99AHG" (0.000147)
641. feature "BAG3" (0.000147)
642. feature "ZYX" (0.000147)
643. feature "MYO9A" (0.000147)
644. feature "FAM83H" (0.000147)
645. feature "LCMT1" (0.000146)
646. feature "PEA15" (0.000146)
647. feature "CS" (0.000145)
648. feature "SLC25A23" (0.000145)
649. feature "FGFR1OP2" (0.000145)
650. feature "PLCB4" (0.000145)
651. feature "SUSD6" (0.000144)
652. feature "CLOCK" (0.000144)
653. feature "CDIPT" (0.000143)
654. feature "ENOX2" (0.000143)
655. feature "NUP62" (0.000143)
656. feature "POLDIP2" (0.000143)
657. feature "CBFA2T3" (0.000142)
658. feature "DDX19A" (0.000142)
659. feature "SAC3D1" (0.000142)
660. feature "TOP1MT" (0.000141)
661. feature "UTP3" (0.000141)
662. feature "FARP1" (0.000141)
663. feature "LTBR" (0.000141)
664. feature "C11orf24" (0.000140)
665. feature "CDC42BPA" (0.000140)
666. feature "KCTD20" (0.000140)
667. feature "MCM2" (0.000140)
668. feature "ZNF688" (0.000139)
669. feature "PPP2R2C" (0.000139)
670. feature "PKP3" (0.000138)
671. feature "PICALM" (0.000138)
672. feature "LCLAT1" (0.000138)
673. feature "MIR4458HG" (0.000137)
674. feature "ULK1" (0.000137)
675. feature "SLC48A1" (0.000137)
676. feature "WWC3" (0.000137)
677. feature "GYS1" (0.000137)
678. feature "LMX1B" (0.000136)
679. feature "PRKX" (0.000136)
680. feature "MPHOSPH9" (0.000136)
681. feature "HMGN5" (0.000136)
682. feature "AATF" (0.000136)
683. feature "RIPK2" (0.000136)
684. feature "FEM1A" (0.000136)
685. feature "NACC1" (0.000135)
686. feature "RP9" (0.000135)
687. feature "DHX37" (0.000135)
688. feature "PCF11" (0.000135)
689. feature "CTSL" (0.000135)
690. feature "MTMR12" (0.000135)
691. feature "ZHX3" (0.000134)
692. feature "DNMT3A" (0.000133)
693. feature "SENP5" (0.000133)
694. feature "SNX24" (0.000133)
695. feature "MGAT4B" (0.000133)
696. feature "NCBP3" (0.000133)
697. feature "ATF5" (0.000132)
698. feature "CHAF1A" (0.000132)
699. feature "RCC1L" (0.000132)
700. feature "CNDP2" (0.000132)
701. feature "PYGO2" (0.000131)
702. feature "UBE2Q2" (0.000131)
703. feature "MPP7" (0.000131)
704. feature "LINC00205" (0.000131)
705. feature "SIX4" (0.000130)
706. feature "PATJ" (0.000130)
707. feature "MSMB" (0.000130)
708. feature "PI4KB" (0.000130)
709. feature "TRIP13" (0.000130)
710. feature "KIAA1522" (0.000130)
711. feature "TNIP1" (0.000130)
712. feature "C16orf72" (0.000130)
713. feature "PARD3" (0.000129)
714. feature "PHF12" (0.000129)
715. feature "PCCA-DT" (0.000129)
716. feature "LINC01278" (0.000129)
717. feature "ARHGEF26" (0.000129)
718. feature "UBXN2B" (0.000128)
719. feature "PPP2R5A" (0.000128)
720. feature "MYB" (0.000127)
721. feature "TYW3" (0.000127)
722. feature "RAB5C" (0.000127)
723. feature "CNOT3" (0.000127)
724. feature "RARG" (0.000127)
725. feature "SINHCAFP3" (0.000127)
726. feature "ZNF326" (0.000126)
727. feature "C20orf27" (0.000126)
728. feature "LARP1B" (0.000126)
729. feature "MIEN1" (0.000126)
730. feature "MBD6" (0.000126)
731. feature "TAF9B" (0.000126)
732. feature "SLMAP" (0.000126)
733. feature "PSMD13" (0.000125)
734. feature "LIMK1" (0.000125)
735. feature "ERF" (0.000125)
736. feature "KIF16B" (0.000125)
737. feature "MAGEF1" (0.000125)
738. feature "FOXP4" (0.000124)
739. feature "PIP4K2A" (0.000124)
740. feature "ZNF252P" (0.000124)
741. feature "ZNFX1" (0.000123)
742. feature "FAM104A" (0.000123)
743. feature "THEM4" (0.000123)
744. feature "MAEA" (0.000123)
745. feature "NCOA2" (0.000123)
746. feature "H2AX" (0.000122)
747. feature "TMEM164" (0.000122)
748. feature "DNAJA3" (0.000122)
749. feature "RAB29" (0.000122)
750. feature "IFI27L2" (0.000122)
751. feature "GDI1" (0.000122)
752. feature "WDR5" (0.000121)
753. feature "PPP1R3D" (0.000121)
754. feature "EFNA5" (0.000121)
755. feature "INF2" (0.000121)
756. feature "SLC25A48" (0.000121)
757. feature "TMEM184C" (0.000120)
758. feature "PSMD5" (0.000120)
759. feature "LMNB2" (0.000120)
760. feature "CEP83" (0.000120)
761. feature "RBAK" (0.000120)
762. feature "UBP1" (0.000119)
763. feature "EP400P1" (0.000119)
764. feature "EIF1AD" (0.000119)
765. feature "RAB1B" (0.000119)
766. feature "NF2" (0.000119)
767. feature "GMFB" (0.000119)
768. feature "PALLD" (0.000118)
769. feature "DCTN6-DT" (0.000118)
770. feature "SCYL3" (0.000118)
771. feature "COG4" (0.000118)
772. feature "GMCL1" (0.000117)
773. feature "WDR82" (0.000117)
774. feature "VCPIP1" (0.000117)
775. feature "EPN1" (0.000117)
776. feature "PANK3" (0.000116)
777. feature "PALB2" (0.000116)
778. feature "RAB12" (0.000116)
779. feature "CBX4" (0.000116)
780. feature "UBE2E3" (0.000116)
781. feature "GYG1" (0.000116)
782. feature "ZNF282" (0.000115)
783. feature "CREB3L2" (0.000115)
784. feature "RAB5B" (0.000115)
785. feature "GRIPAP1" (0.000115)
786. feature "HAUS1" (0.000115)
787. feature "SNAPC5" (0.000115)
788. feature "RAD23A" (0.000115)
789. feature "VAV2" (0.000115)
790. feature "SOS2" (0.000115)
791. feature "ABL1" (0.000115)
792. feature "FBXW4" (0.000114)
793. feature "PACSIN3" (0.000114)
794. feature "ISY1" (0.000114)
795. feature "TTLL12" (0.000114)
796. feature "PCNX2" (0.000114)
797. feature "HMBS" (0.000114)
798. feature "MED1" (0.000114)
799. feature "ZNF609" (0.000113)
800. feature "FASTK" (0.000113)
801. feature "LRCH3" (0.000113)
802. feature "OPTN" (0.000113)
803. feature "SPIRE1" (0.000113)
804. feature "CEP63" (0.000113)
805. feature "CCNQ" (0.000113)
806. feature "MT-TP" (0.000113)
807. feature "RGPD4-AS1" (0.000113)
808. feature "ZMYM3" (0.000113)
809. feature "MYH14" (0.000112)
810. feature "BLOC1S3" (0.000112)
811. feature "CHAMP1" (0.000112)
812. feature "BMPR1B" (0.000112)
813. feature "TCHP" (0.000112)
814. feature "PABPN1" (0.000111)
815. feature "ARHGAP11A" (0.000111)
816. feature "NEMP1" (0.000111)
817. feature "ARHGAP1" (0.000111)
818. feature "ZBTB10" (0.000111)
819. feature "HOMER3" (0.000110)
820. feature "CRK" (0.000110)
821. feature "GLE1" (0.000110)
822. feature "NELFB" (0.000110)
823. feature "POLE" (0.000110)
824. feature "HCG18" (0.000109)
825. feature "VRK3" (0.000109)
826. feature "ACOT7" (0.000109)
827. feature "MT-TY" (0.000109)
828. feature "FMN1" (0.000109)
829. feature "SETD1B" (0.000108)
830. feature "ZFAND2A" (0.000108)
831. feature "RESF1" (0.000108)
832. feature "PKP4" (0.000108)
833. feature "CMPK1" (0.000108)
834. feature "RASSF3" (0.000107)
835. feature "RNF34" (0.000107)
836. feature "RFWD3" (0.000107)
837. feature "VPS4A" (0.000107)
838. feature "MARVELD3" (0.000107)
839. feature "COTL1" (0.000107)
840. feature "AGPAT2" (0.000107)
841. feature "DBNDD1" (0.000107)
842. feature "CHMP1A" (0.000107)
843. feature "DCP1A" (0.000106)
844. feature "ZNF26" (0.000106)
845. feature "HELB" (0.000106)
846. feature "UHRF1BP1" (0.000106)
847. feature "SHOX" (0.000106)
848. feature "RNF4" (0.000106)
849. feature "DCLRE1B" (0.000105)
850. feature "FAM214A" (0.000105)
851. feature "LETM1" (0.000105)
852. feature "SSH1" (0.000104)
853. feature "PET117" (0.000103)
854. feature "IRF2BPL" (0.000103)
855. feature "ZNF354A" (0.000103)
856. feature "ANKRD40" (0.000103)
857. feature "SIAH2" (0.000103)
858. feature "GDAP2" (0.000103)
859. feature "LINC02511" (0.000102)
860. feature "YTHDF1" (0.000102)
861. feature "ZNF512B" (0.000102)
862. feature "NUP98" (0.000102)
863. feature "ADGRV1" (0.000102)
864. feature "PIP4P1" (0.000102)
865. feature "PITX1" (0.000101)
866. feature "ABLIM1" (0.000101)
867. feature "SRSF1" (0.000101)
868. feature "ORAI1" (0.000100)
869. feature "USP25" (0.000100)
870. feature "RAB3D" (0.000100)
871. feature "WDR46" (0.000100)
872. feature "KDM1B" (0.000100)
873. feature "PIK3CB" (0.000100)
874. feature "PSMD3" (0.000100)
875. feature "FBXL19" (0.000100)
876. feature "TMCC1" (0.000099)
877. feature "PLEKHF2" (0.000099)
878. feature "CACNG4" (0.000099)
879. feature "HIF3A" (0.000099)
880. feature "ARL13B" (0.000099)
881. feature "ELP3" (0.000098)
882. feature "UBALD1" (0.000098)
883. feature "RHPN1" (0.000098)
884. feature "STK11" (0.000098)
885. feature "SALL4" (0.000098)
886. feature "CNOT6L" (0.000098)
887. feature "WDFY3" (0.000098)
888. feature "RPL12P38" (0.000098)
889. feature "RELA" (0.000098)
890. feature "DCTN5" (0.000098)
891. feature "FOXC1" (0.000097)
892. feature "CEP135" (0.000097)
893. feature "FAM102B" (0.000097)
894. feature "SAMD4B" (0.000097)
895. feature "MLLT6" (0.000097)
896. feature "NETO2" (0.000097)
897. feature "PITPNA" (0.000097)
898. feature "EHBP1" (0.000096)
899. feature "MFN2" (0.000096)
900. feature "EIPR1" (0.000096)
901. feature "THRB" (0.000096)
902. feature "COL12A1" (0.000096)
903. feature "HIP1" (0.000096)
904. feature "KLHL8" (0.000096)
905. feature "NGRN" (0.000096)
906. feature "PRR12" (0.000096)
907. feature "ITSN1" (0.000096)
908. feature "AKR1C2" (0.000096)
909. feature "PRDM4" (0.000096)
910. feature "DVL3" (0.000096)
911. feature "HOMER2" (0.000095)
912. feature "SDHAF2" (0.000095)
913. feature "HMGA2" (0.000095)
914. feature "ZNF526" (0.000095)
915. feature "STK25" (0.000095)
916. feature "MYEF2" (0.000094)
917. feature "RAPGEFL1" (0.000094)
918. feature "SECISBP2L" (0.000094)
919. feature "ZDHHC5" (0.000094)
920. feature "LATS2" (0.000094)
921. feature "RNASEH1" (0.000094)
922. feature "PIGS" (0.000094)
923. feature "TNFSF13B" (0.000094)
924. feature "RAI1" (0.000094)
925. feature "NSMCE4A" (0.000094)
926. feature "AKT1S1" (0.000094)
927. feature "ZWILCH" (0.000093)
928. feature "PSMF1" (0.000093)
929. feature "ZCCHC2" (0.000093)
930. feature "PAQR7" (0.000093)
931. feature "DCAF10" (0.000093)
932. feature "CDC25B" (0.000093)
933. feature "CDR2L" (0.000093)
934. feature "CIP2A" (0.000093)
935. feature "SHISA5" (0.000092)
936. feature "BCL3" (0.000092)
937. feature "FOXJ3" (0.000092)
938. feature "ZNF462" (0.000092)
939. feature "AZI2" (0.000092)
940. feature "AXL" (0.000092)
941. feature "IRX2" (0.000091)
942. feature "ZNF562" (0.000091)
943. feature "PCGF3" (0.000091)
944. feature "L2HGDH" (0.000091)
945. feature "MIPOL1" (0.000091)
946. feature "WDR26" (0.000091)
947. feature "CAMKK2" (0.000091)
948. feature "ZADH2" (0.000091)
949. feature "SSH3" (0.000091)
950. feature "SPATA33" (0.000091)
951. feature "ZDHHC7" (0.000091)
952. feature "RIC8A" (0.000091)
953. feature "MAP3K9" (0.000091)
954. feature "SAP130" (0.000090)
955. feature "BICD2" (0.000090)
956. feature "PAQR8" (0.000090)
957. feature "IFT74" (0.000090)
958. feature "PPP1R37" (0.000090)
959. feature "SNX4" (0.000090)
960. feature "MIS12" (0.000090)
961. feature "SPAG1" (0.000090)
962. feature "TMEM250" (0.000089)
963. feature "SMG5" (0.000089)
964. feature "RAD54L2" (0.000089)
965. feature "ZBTB37" (0.000089)
966. feature "ADARB1" (0.000089)
967. feature "TMPRSS4" (0.000089)
968. feature "TF" (0.000089)
969. feature "GRK6" (0.000089)
970. feature "ZDHHC6" (0.000089)
971. feature "USP6NL" (0.000089)
972. feature "BRF1" (0.000089)
973. feature "FBXL18" (0.000089)
974. feature "ZZZ3" (0.000089)
975. feature "CNTRL" (0.000088)
976. feature "SMIM27" (0.000088)
977. feature "FAM126B" (0.000088)
978. feature "CDCA4" (0.000088)
979. feature "TEX261" (0.000088)
980. feature "RGL2" (0.000088)
981. feature "SYNJ2" (0.000088)
982. feature "GDPGP1" (0.000088)
983. feature "CCDC149" (0.000088)
984. feature "CMBL" (0.000088)
985. feature "RFK" (0.000088)
986. feature "ZRANB1" (0.000087)
987. feature "OTUD4" (0.000087)
988. feature "UBL4A" (0.000087)
989. feature "PAK4" (0.000087)
990. feature "STK40" (0.000087)
991. feature "RHBDD2" (0.000087)
992. feature "PCNX3" (0.000087)
993. feature "CHERP" (0.000087)
994. feature "ZNF444" (0.000086)
995. feature "NLK" (0.000086)
996. feature "PHC3" (0.000086)
997. feature "PAPSS2" (0.000086)
998. feature "TMSB4XP4" (0.000086)
999. feature "NPLOC4" (0.000086)
1000. feature "ABCF2" (0.000086)
1001. feature "PREB" (0.000086)
1002. feature "TDRD3" (0.000085)
1003. feature "ZNF544" (0.000085)
1004. feature "DYRK1B" (0.000085)
1005. feature "LIN54" (0.000085)
1006. feature "FBXO11" (0.000085)
1007. feature "NECTIN1" (0.000085)
1008. feature "LRRCC1" (0.000085)
1009. feature "INPPL1" (0.000085)
1010. feature "MTMR2" (0.000085)
1011. feature "EPC2" (0.000085)
1012. feature "C2CD2L" (0.000085)
1013. feature "LCORL" (0.000085)
1014. feature "PLA2G12A" (0.000084)
1015. feature "HS6ST1" (0.000084)
1016. feature "ADAMTS19" (0.000084)
1017. feature "DTX3L" (0.000084)
1018. feature "GAREM1" (0.000084)
1019. feature "PAN3" (0.000084)
1020. feature "FDX2" (0.000084)
1021. feature "SLC52A2" (0.000084)
1022. feature "KLHL7" (0.000084)
1023. feature "PDE8A" (0.000084)
1024. feature "ZNF85" (0.000083)
1025. feature "KCTD15" (0.000083)
1026. feature "SRF" (0.000083)
1027. feature "ESRP2" (0.000083)
1028. feature "AKAP17A" (0.000083)
1029. feature "SLC35C2" (0.000083)
1030. feature "YARS1" (0.000083)
1031. feature "AKT2" (0.000083)
1032. feature "MTMR4" (0.000083)
1033. feature "INO80E" (0.000082)
1034. feature "VAT1" (0.000082)
1035. feature "CEP128" (0.000082)
1036. feature "AVEN" (0.000082)
1037. feature "ZBTB34" (0.000082)
1038. feature "BCAR1" (0.000082)
1039. feature "LYPLA2" (0.000082)
1040. feature "ATXN1L" (0.000082)
1041. feature "ZNF91" (0.000082)
1042. feature "KIF13A" (0.000081)
1043. feature "ZNF813" (0.000081)
1044. feature "SRFBP1" (0.000081)
1045. feature "ROGDI" (0.000081)
1046. feature "SEPTIN8" (0.000081)
1047. feature "TRAM2" (0.000081)
1048. feature "MRPS2" (0.000081)
1049. feature "BOP1" (0.000081)
1050. feature "ITGA3" (0.000081)
1051. feature "ADAT1" (0.000081)
1052. feature "CPSF4" (0.000081)
1053. feature "NDEL1" (0.000080)
1054. feature "FAM13B" (0.000080)
1055. feature "NIPSNAP3A" (0.000080)
1056. feature "FNTA" (0.000080)
1057. feature "TRMT12" (0.000080)
1058. feature "BCL2L15" (0.000080)
1059. feature "AGAP3" (0.000080)
1060. feature "FAM47E" (0.000080)
1061. feature "RAB35" (0.000080)
1062. feature "AMOTL1" (0.000080)
1063. feature "BICD1" (0.000080)
1064. feature "MAPKAPK5" (0.000079)
1065. feature "TACC2" (0.000079)
1066. feature "ARHGAP26" (0.000079)
1067. feature "CAMSAP1" (0.000079)
1068. feature "ZNF629" (0.000079)
1069. feature "UIMC1" (0.000079)
1070. feature "RECQL4" (0.000079)
1071. feature "BRPF3" (0.000079)
1072. feature "TP53INP1" (0.000079)
1073. feature "PACS2" (0.000079)
1074. feature "TARDBP" (0.000079)
1075. feature "CCNC" (0.000078)
1076. feature "NMI" (0.000078)
1077. feature "JMJD8" (0.000078)
1078. feature "RNF32" (0.000078)
1079. feature "SYT14" (0.000078)
1080. feature "CEP162" (0.000078)
1081. feature "PITPNC1" (0.000078)
1082. feature "ZNF385A" (0.000078)
1083. feature "CLK2" (0.000078)
1084. feature "CEBPB" (0.000078)
1085. feature "CAD" (0.000077)
1086. feature "MT-TD" (0.000077)
1087. feature "FAM222B" (0.000077)
1088. feature "NR1H2" (0.000077)
1089. feature "TJAP1" (0.000077)
1090. feature "MYD88" (0.000077)
1091. feature "TJP3" (0.000077)
1092. feature "PHLDA1" (0.000077)
1093. feature "CDK7" (0.000076)
1094. feature "CSNK2A2" (0.000076)
1095. feature "NAA16" (0.000076)
1096. feature "NEUROD2" (0.000076)
1097. feature "MNT" (0.000076)
1098. feature "ZNF48" (0.000076)
1099. feature "VRK2" (0.000076)
1100. feature "HPS1" (0.000076)
1101. feature "OPRM1" (0.000076)
1102. feature "MGLL" (0.000076)
1103. feature "YTHDF3" (0.000076)
1104. feature "RAP2A" (0.000076)
1105. feature "MAPK9" (0.000075)
1106. feature "CDK6" (0.000075)
1107. feature "RC3H2" (0.000075)
1108. feature "EFHD1" (0.000075)
1109. feature "RAB17" (0.000075)
1110. feature "BICDL1" (0.000075)
1111. feature "TATDN2" (0.000075)
1112. feature "TMEM69" (0.000075)
1113. feature "ZUP1" (0.000075)
1114. feature "GTF3C2" (0.000075)
1115. feature "TTLL5" (0.000075)
1116. feature "SLC25A44" (0.000074)
1117. feature "CLEC16A" (0.000074)
1118. feature "TP53BP2" (0.000074)
1119. feature "IMPA2" (0.000074)
1120. feature "XRCC2" (0.000074)
1121. feature "MASTL" (0.000074)
1122. feature "GIT1" (0.000073)
1123. feature "TMEM259" (0.000073)
1124. feature "EDEM1" (0.000073)
1125. feature "FOXM1" (0.000073)
1126. feature "PHF21A" (0.000073)
1127. feature "WDFY2" (0.000073)
1128. feature "RNF220" (0.000073)
1129. feature "TEAD1" (0.000073)
1130. feature "FGF12" (0.000073)
1131. feature "AFF1" (0.000073)
1132. feature "FNBP1P1" (0.000073)
1133. feature "PLAA" (0.000073)
1134. feature "PM20D2" (0.000073)
1135. feature "ZNF510" (0.000073)
1136. feature "ZHX2" (0.000073)
1137. feature "TCF20" (0.000072)
1138. feature "PPRC1" (0.000072)
1139. feature "KLF9" (0.000072)
1140. feature "GADD45A" (0.000072)
1141. feature "MT-TN" (0.000072)
1142. feature "MIOS" (0.000072)
1143. feature "SAMD8" (0.000072)
1144. feature "NKAPD1" (0.000072)
1145. feature "RASGEF1B" (0.000072)
1146. feature "NKIRAS2" (0.000072)
1147. feature "TET3" (0.000072)
1148. feature "TTL" (0.000071)
1149. feature "FAM189B" (0.000071)
1150. feature "IPO4" (0.000071)
1151. feature "PLEKHH3" (0.000071)
1152. feature "TPRA1" (0.000071)
1153. feature "GTPBP6" (0.000071)
1154. feature "SPIRE2" (0.000071)
1155. feature "HPCAL1" (0.000071)
1156. feature "DGKZ" (0.000071)
1157. feature "CAPN15" (0.000071)
1158. feature "LIAS" (0.000071)
1159. feature "TIAM1" (0.000071)
1160. feature "TRIB3" (0.000071)
1161. feature "AFG3L1P" (0.000071)
1162. feature "SERINC5" (0.000071)
1163. feature "B3GALT9" (0.000070)
1164. feature "NSMAF" (0.000070)
1165. feature "ARHGAP21" (0.000070)
1166. feature "TRAF3IP2" (0.000070)
1167. feature "MAP7D1" (0.000070)
1168. feature "LINC00491" (0.000070)
1169. feature "SLC4A1" (0.000070)
1170. feature "RFC5" (0.000070)
1171. feature "ERAP2" (0.000069)
1172. feature "SPRYD3" (0.000069)
1173. feature "SLC35A4" (0.000069)
1174. feature "ZSCAN18" (0.000069)
1175. feature "EFNA2" (0.000069)
1176. feature "FAM20B" (0.000069)
1177. feature "TIMM29" (0.000069)
1178. feature "SLAIN2" (0.000069)
1179. feature "IMPA1" (0.000069)
1180. feature "HSPA2" (0.000069)
1181. feature "SERPINE1" (0.000069)
1182. feature "CEP104" (0.000069)
1183. feature "TBC1D9" (0.000069)
1184. feature "ZNF620" (0.000068)
1185. feature "SPACA6" (0.000068)
1186. feature "CCDC18" (0.000068)
1187. feature "PCLO" (0.000068)
1188. feature "CCDC88C" (0.000068)
1189. feature "FRMD8" (0.000068)
1190. feature "KLHL20" (0.000068)
1191. feature "ABL2" (0.000068)
1192. feature "ATP6V1H" (0.000068)
1193. feature "PRXL2C" (0.000068)
1194. feature "ZNF605" (0.000068)
1195. feature "TSHZ2" (0.000068)
1196. feature "MYADM" (0.000068)
1197. feature "MAPK3" (0.000068)
1198. feature "SACS" (0.000068)
1199. feature "ASF1B" (0.000067)
1200. feature "SPATA20" (0.000067)
1201. feature "BTBD6" (0.000067)
1202. feature "IRGQ" (0.000067)
1203. feature "ARAF" (0.000067)
1204. feature "SRD5A1" (0.000067)
1205. feature "PLCD3" (0.000067)
1206. feature "STUB1" (0.000067)
1207. feature "RAD51" (0.000067)
1208. feature "E2F4" (0.000067)
1209. feature "DRAM2" (0.000067)
1210. feature "DUSP8" (0.000067)
1211. feature "PROSER3" (0.000067)
1212. feature "TNK2" (0.000067)
1213. feature "ZNF398" (0.000067)
1214. feature "C22orf46" (0.000067)
1215. feature "ZZEF1" (0.000066)
1216. feature "FOXK1" (0.000066)
1217. feature "UBA6-DT" (0.000066)
1218. feature "ZNF777" (0.000066)
1219. feature "ZNF347" (0.000066)
1220. feature "TTC28" (0.000066)
1221. feature "YJU2" (0.000066)
1222. feature "PLD6" (0.000066)
1223. feature "FARP2" (0.000066)
1224. feature "SRP68" (0.000066)
1225. feature "EGR3" (0.000065)
1226. feature "TNPO2" (0.000065)
1227. feature "SLC25A28" (0.000065)
1228. feature "USP45" (0.000065)
1229. feature "EPHB4" (0.000065)
1230. feature "TRAF6" (0.000065)
1231. feature "MANBAL" (0.000065)
1232. feature "CEP131" (0.000065)
1233. feature "USP24" (0.000065)
1234. feature "PARP9" (0.000065)
1235. feature "NPTN" (0.000065)
1236. feature "TIMM22" (0.000065)
1237. feature "AFAP1" (0.000065)
1238. feature "ANXA6" (0.000065)
1239. feature "ATXN2L" (0.000065)
1240. feature "SF3A2" (0.000064)
1241. feature "GREM1" (0.000064)
1242. feature "GLIS2" (0.000064)
1243. feature "PIK3CA" (0.000064)
1244. feature "UBQLN4" (0.000064)
1245. feature "MSR1" (0.000064)
1246. feature "BOK" (0.000064)
1247. feature "MYO9B" (0.000064)
1248. feature "VPS54" (0.000064)
1249. feature "MTND2P28" (0.000064)
1250. feature "NPRL3" (0.000063)
1251. feature "SGTA" (0.000063)
1252. feature "PPP1R12B" (0.000063)
1253. feature "C14orf28" (0.000063)
1254. feature "KIAA0895L" (0.000063)
1255. feature "MINK1" (0.000063)
1256. feature "SOX9-AS1" (0.000063)
1257. feature "ARHGAP17" (0.000063)
1258. feature "INTS3" (0.000063)
1259. feature "TIAM2" (0.000063)
1260. feature "SKI" (0.000063)
1261. feature "TPRN" (0.000063)
1262. feature "SMKR1" (0.000063)
1263. feature "UBE2F" (0.000062)
1264. feature "ATXN7L1" (0.000062)
1265. feature "KIAA0040" (0.000062)
1266. feature "GATC" (0.000062)
1267. feature "NCOA1" (0.000062)
1268. feature "JADE2" (0.000062)
1269. feature "CCNB3P1" (0.000062)
1270. feature "INPP5A" (0.000062)
1271. feature "PSCA" (0.000062)
1272. feature "RRP12" (0.000062)
1273. feature "RPS6KA4" (0.000062)
1274. feature "CACNB3" (0.000062)
1275. feature "ZNF580" (0.000062)
1276. feature "ANKS6" (0.000062)
1277. feature "DPP9" (0.000062)
1278. feature "RPLP0P2" (0.000062)
1279. feature "RILPL2" (0.000062)
1280. feature "KSR2" (0.000062)
1281. feature "KLLN" (0.000061)
1282. feature "ATG101" (0.000061)
1283. feature "PHF8" (0.000061)
1284. feature "SAMD12" (0.000061)
1285. feature "CDK8" (0.000061)
1286. feature "SSX2IP" (0.000061)
1287. feature "BRMS1L" (0.000061)
1288. feature "TIGAR" (0.000061)
1289. feature "MTARC1" (0.000061)
1290. feature "SIPA1L2" (0.000061)
1291. feature "FGFBP3" (0.000061)
1292. feature "DYRK2" (0.000061)
1293. feature "TCHH" (0.000061)
1294. feature "CRKL" (0.000061)
1295. feature "MAN2B2" (0.000061)
1296. feature "MT-TV" (0.000061)
1297. feature "NACC2" (0.000060)
1298. feature "RAB30" (0.000060)
1299. feature "NEUROD1" (0.000060)
1300. feature "THAP1" (0.000060)
1301. feature "LINC00173" (0.000060)
1302. feature "SLC36A2" (0.000060)
1303. feature "CASTOR2" (0.000060)
1304. feature "CES2" (0.000060)
1305. feature "PPP1R8" (0.000060)
1306. feature "BTRC" (0.000060)
1307. feature "SLC43A2" (0.000060)
1308. feature "SLC2A11" (0.000060)
1309. feature "BDH1" (0.000060)
1310. feature "SLITRK5" (0.000060)
1311. feature "CERS2" (0.000059)
1312. feature "PTPN13" (0.000059)
1313. feature "RUNX2" (0.000059)
1314. feature "ITPR3" (0.000059)
1315. feature "RPSAP48" (0.000059)
1316. feature "VPS9D1-AS1" (0.000059)
1317. feature "PRR36" (0.000059)
1318. feature "SPINDOC" (0.000059)
1319. feature "ASB6" (0.000059)
1320. feature "MNS1" (0.000059)
1321. feature "RNF122" (0.000059)
1322. feature "EPS8L1" (0.000059)
1323. feature "LAT2" (0.000059)
1324. feature "GCLC" (0.000059)
1325. feature "NBEAL2" (0.000059)
1326. feature "PROM2" (0.000058)
1327. feature "PHKG2" (0.000058)
1328. feature "NEDD1" (0.000058)
1329. feature "ABR" (0.000058)
1330. feature "PRPS1" (0.000058)
1331. feature "ZNF467" (0.000058)
1332. feature "ASB8" (0.000058)
1333. feature "ZNF764" (0.000058)
1334. feature "ZNF461" (0.000058)
1335. feature "TGFB1" (0.000058)
1336. feature "KCTD18" (0.000058)
1337. feature "TGFB2" (0.000058)
1338. feature "SMIM19" (0.000058)
1339. feature "PGAM5" (0.000058)
1340. feature "PRKAG1" (0.000058)
1341. feature "ENTPD1" (0.000058)
1342. feature "MYO18A" (0.000058)
1343. feature "SMIM1" (0.000058)
1344. feature "OXLD1" (0.000058)
1345. feature "RAI14" (0.000057)
1346. feature "CC2D1A" (0.000057)
1347. feature "OR4F17" (0.000057)
1348. feature "YJEFN3" (0.000057)
1349. feature "POLK" (0.000057)
1350. feature "TSSC4" (0.000057)
1351. feature "DBT" (0.000057)
1352. feature "GNPDA2" (0.000057)
1353. feature "MAP3K3" (0.000057)
1354. feature "FDXR" (0.000057)
1355. feature "IER5L" (0.000057)
1356. feature "EPN3" (0.000057)
1357. feature "SLC25A25-AS1" (0.000057)
1358. feature "ZMAT3" (0.000057)
1359. feature "RBM38" (0.000057)
1360. feature "CDH6" (0.000056)
1361. feature "ACKR3" (0.000056)
1362. feature "BAIAP2-DT" (0.000056)
1363. feature "JPH1" (0.000056)
1364. feature "MIEF1" (0.000056)
1365. feature "ONECUT2" (0.000056)
1366. feature "AGO1" (0.000056)
1367. feature "MBNL3" (0.000056)
1368. feature "KBTBD6" (0.000056)
1369. feature "SLC26A4-AS1" (0.000056)
1370. feature "HGH1" (0.000056)
1371. feature "DIS3L" (0.000056)
1372. feature "TRERF1" (0.000056)
1373. feature "USP38" (0.000056)
1374. feature "KCNJ15" (0.000056)
1375. feature "CRAMP1" (0.000056)
1376. feature "RETSAT" (0.000055)
1377. feature "UBE2D4" (0.000055)
1378. feature "LUZP1" (0.000055)
1379. feature "MGAT4A" (0.000055)
1380. feature "METTL22" (0.000055)
1381. feature "PDE12" (0.000055)
1382. feature "ELP1" (0.000055)
1383. feature "AKAP5" (0.000055)
1384. feature "LCA5L" (0.000055)
1385. feature "RAB27A" (0.000055)
1386. feature "NFX1" (0.000055)
1387. feature "DUS4L" (0.000055)
1388. feature "PML" (0.000055)
1389. feature "TPGS1" (0.000055)
1390. feature "ZNF731P" (0.000055)
1391. feature "QRSL1" (0.000055)
1392. feature "DDIAS" (0.000055)
1393. feature "MDM1" (0.000055)
1394. feature "CIAO3" (0.000055)
1395. feature "KIAA1549" (0.000055)
1396. feature "TRAK2" (0.000055)
1397. feature "TRIM11" (0.000055)
1398. feature "WDR73" (0.000054)
1399. feature "PHACTR1" (0.000054)
1400. feature "PLCB1" (0.000054)
1401. feature "WWC1" (0.000054)
1402. feature "EPAS1" (0.000054)
1403. feature "PSMG3-AS1" (0.000054)
1404. feature "ERVK13-1" (0.000054)
1405. feature "ZNF362" (0.000054)
1406. feature "L3MBTL2-AS1" (0.000054)
1407. feature "CCDC7" (0.000054)
1408. feature "ZNF768" (0.000054)
1409. feature "TFE3" (0.000054)
1410. feature "SEMA4C" (0.000054)
1411. feature "NHS" (0.000053)
1412. feature "FAAP100" (0.000053)
1413. feature "LDLRAP1" (0.000053)
1414. feature "SLC25A51" (0.000053)
1415. feature "CAMSAP3" (0.000053)
1416. feature "SOX13" (0.000053)
1417. feature "CTPS2" (0.000053)
1418. feature "GPATCH1" (0.000053)
1419. feature "MRPL20-AS1" (0.000053)
1420. feature "ANKH" (0.000053)
1421. feature "PKMYT1" (0.000053)
1422. feature "GOLT1A" (0.000053)
1423. feature "LNCARSR" (0.000053)
1424. feature "KMT2B" (0.000053)
1425. feature "ZKSCAN5" (0.000053)
1426. feature "LSM10" (0.000053)
1427. feature "CCM2" (0.000053)
1428. feature "FOXJ2" (0.000053)
1429. feature "SYNGAP1" (0.000052)
1430. feature "DLG5" (0.000052)
1431. feature "TSPAN17" (0.000052)
1432. feature "ZBTB2" (0.000052)
1433. feature "TBC1D10A" (0.000052)
1434. feature "DONSON" (0.000052)
1435. feature "KRT17" (0.000052)
1436. feature "AK7" (0.000052)
1437. feature "MED25" (0.000052)
1438. feature "FRMD3" (0.000052)
1439. feature "TAF6" (0.000052)
1440. feature "TTC7A" (0.000052)
1441. feature "PNMA1" (0.000052)
1442. feature "DENND1B" (0.000052)
1443. feature "SLC13A5" (0.000052)
1444. feature "C16orf91" (0.000052)
1445. feature "FAM193B" (0.000052)
1446. feature "PINLYP" (0.000052)
1447. feature "EYA3" (0.000052)
1448. feature "LAT" (0.000052)
1449. feature "ALAD" (0.000051)
1450. feature "FTO" (0.000051)
1451. feature "DPH1" (0.000051)
1452. feature "PPP4R1" (0.000051)
1453. feature "ATP1A4" (0.000051)
1454. feature "VPS16" (0.000051)
1455. feature "RNFT2" (0.000051)
1456. feature "BEND7" (0.000051)
1457. feature "PATL1" (0.000051)
1458. feature "PRKCD" (0.000051)
1459. feature "PCED1B-AS1" (0.000051)
1460. feature "CEP170B" (0.000051)
1461. feature "HAND2-AS1" (0.000051)
1462. feature "C3orf62" (0.000051)
1463. feature "FBXO7" (0.000051)
1464. feature "CARD10" (0.000051)
1465. feature "DOCK8-AS1" (0.000051)
1466. feature "ATXN7L3" (0.000051)
1467. feature "SBF2" (0.000051)
1468. feature "NDST1" (0.000051)
1469. feature "CASKIN2" (0.000051)
1470. feature "PTAR1" (0.000051)
1471. feature "MROH1" (0.000050)
1472. feature "SAMD15" (0.000050)
1473. feature "ZMIZ2" (0.000050)
1474. feature "SOX11" (0.000050)
1475. feature "GUCD1" (0.000050)
1476. feature "CNPPD1" (0.000050)
1477. feature "RRN3P3" (0.000050)
1478. feature "LIN52" (0.000050)
1479. feature "RNF130" (0.000050)
1480. feature "NOL4L" (0.000050)
1481. feature "PGAP6" (0.000050)
1482. feature "CAMK2D" (0.000050)
1483. feature "DPF1" (0.000049)
1484. feature "GPR158" (0.000049)
1485. feature "ZNF154" (0.000049)
1486. feature "KRT86" (0.000049)
1487. feature "FAR1" (0.000049)
1488. feature "C11orf71" (0.000049)
1489. feature "FLOT2" (0.000049)
1490. feature "TEDC2-AS1" (0.000049)
1491. feature "TBC1D13" (0.000049)
1492. feature "ZFP36" (0.000049)
1493. feature "LINC02822" (0.000049)
1494. feature "LSS" (0.000049)
1495. feature "HOXC13" (0.000049)
1496. feature "MAILR" (0.000049)
1497. feature "NEB" (0.000049)
1498. feature "OR7E13P" (0.000049)
1499. feature "RBMS3-AS3" (0.000049)
1500. feature "PXYLP1" (0.000049)
1501. feature "ZFAND2B" (0.000049)
1502. feature "KLC2" (0.000049)
1503. feature "CRYL1" (0.000049)
1504. feature "HID1" (0.000049)
1505. feature "FBXL20" (0.000049)
1506. feature "KDF1" (0.000049)
1507. feature "BACH2" (0.000048)
1508. feature "HLCS" (0.000048)
1509. feature "EVPL" (0.000048)
1510. feature "PABPC1L" (0.000048)
1511. feature "ZNF574" (0.000048)
1512. feature "ZNF142" (0.000048)
1513. feature "HES6" (0.000048)
1514. feature "POLR3E" (0.000048)
1515. feature "TRIM14" (0.000048)
1516. feature "SH2B1" (0.000048)
1517. feature "CLUH" (0.000048)
1518. feature "B9D1" (0.000048)
1519. feature "FBRSL1" (0.000048)
1520. feature "IRX4" (0.000048)
1521. feature "MARK4" (0.000048)
1522. feature "COA7" (0.000048)
1523. feature "RAB39B" (0.000048)
1524. feature "BMP1" (0.000048)
1525. feature "BMS1P23" (0.000047)
1526. feature "RGP1" (0.000047)
1527. feature "DDAH1" (0.000047)
1528. feature "ODF2L" (0.000047)
1529. feature "FAM171A2" (0.000047)
1530. feature "TMEM178B" (0.000047)
1531. feature "TMEM184B" (0.000047)
1532. feature "FAM131A" (0.000047)
1533. feature "JADE3" (0.000047)
1534. feature "PTPDC1" (0.000047)
1535. feature "PTPN9" (0.000047)
1536. feature "MOB3A" (0.000047)
1537. feature "PTPN23" (0.000047)
1538. feature "TP53I11" (0.000047)
1539. feature "ZNF483" (0.000046)
1540. feature "DEAF1" (0.000046)
1541. feature "FIZ1" (0.000046)
1542. feature "ZNF254" (0.000046)
1543. feature "VWA1" (0.000046)
1544. feature "MLX" (0.000046)
1545. feature "RIPOR1" (0.000046)
1546. feature "FOXK2" (0.000046)
1547. feature "SOCS2" (0.000046)
1548. feature "UBE2QL1" (0.000046)
1549. feature "ARHGEF37" (0.000046)
1550. feature "MPDZ" (0.000046)
1551. feature "MBD5" (0.000046)
1552. feature "RAB11FIP4" (0.000046)
1553. feature "MT-TA" (0.000046)
1554. feature "MUCL1" (0.000045)
1555. feature "CIPC" (0.000045)
1556. feature "FBXO42" (0.000045)
1557. feature "MOB1B" (0.000045)
1558. feature "TPH1" (0.000045)
1559. feature "PPP1R3B" (0.000045)
1560. feature "CDK4" (0.000045)
1561. feature "GOLGA2P7" (0.000045)
1562. feature "FLVCR2" (0.000045)
1563. feature "C7orf26" (0.000045)
1564. feature "KAZN" (0.000045)
1565. feature "MTFR1L" (0.000045)
1566. feature "ZNF35" (0.000045)
1567. feature "MMP25-AS1" (0.000045)
1568. feature "PARP6" (0.000045)
1569. feature "COPG2IT1" (0.000045)
1570. feature "NAB1" (0.000045)
1571. feature "DPY19L1P1" (0.000045)
1572. feature "TTC39A" (0.000045)
1573. feature "ZNF702P" (0.000045)
1574. feature "PCDHB9" (0.000044)
1575. feature "FADS3" (0.000044)
1576. feature "TP53INP2" (0.000044)
1577. feature "DCAF4" (0.000044)
1578. feature "STX2" (0.000044)
1579. feature "WDR62" (0.000044)
1580. feature "ACVR1" (0.000044)
1581. feature "TPP1" (0.000044)
1582. feature "PPP2R5D" (0.000044)
1583. feature "ARFGAP1" (0.000044)
1584. feature "ASB13" (0.000044)
1585. feature "ACER3" (0.000044)
1586. feature "PIK3R2" (0.000044)
1587. feature "ARSA" (0.000043)
1588. feature "SCLY" (0.000043)
1589. feature "IQSEC2" (0.000043)
1590. feature "LACTB2-AS1" (0.000043)
1591. feature "DKK1" (0.000043)
1592. feature "SNHG17" (0.000043)
1593. feature "ZNF879" (0.000043)
1594. feature "PYCR3" (0.000043)
1595. feature "WNT7B" (0.000043)
1596. feature "FBXL16" (0.000043)
1597. feature "SH3RF1" (0.000043)
1598. feature "F8" (0.000043)
1599. feature "BAGE2" (0.000043)
1600. feature "LINC00326" (0.000043)
1601. feature "NCOA5" (0.000043)
1602. feature "TPCN1" (0.000043)
1603. feature "GMEB1" (0.000043)
1604. feature "TGFB3" (0.000042)
1605. feature "MLYCD" (0.000042)
1606. feature "COL5A1" (0.000042)
1607. feature "NXF1" (0.000042)
1608. feature "TEPSIN" (0.000042)
1609. feature "C2CD5" (0.000042)
1610. feature "CYP27C1" (0.000042)
1611. feature "LYPD1" (0.000042)
1612. feature "ANKRD42" (0.000042)
1613. feature "CALML5" (0.000042)
1614. feature "SRC" (0.000042)
1615. feature "TBL3" (0.000042)
1616. feature "WT1-AS" (0.000042)
1617. feature "HDAC7" (0.000042)
1618. feature "METTL21A" (0.000041)
1619. feature "IGHMBP2" (0.000041)
1620. feature "FBXO46" (0.000041)
1621. feature "PPP1R9B" (0.000041)
1622. feature "ZBED6" (0.000041)
1623. feature "ZCCHC14" (0.000041)
1624. feature "NOTCH2NLC" (0.000041)
1625. feature "TRIM41" (0.000041)
1626. feature "STK39" (0.000041)
1627. feature "RIC1" (0.000041)
1628. feature "NXNL2" (0.000041)
1629. feature "OXNAD1" (0.000041)
1630. feature "TRAF3" (0.000041)
1631. feature "ZBTB42" (0.000041)
1632. feature "C8orf82" (0.000041)
1633. feature "ARHGEF11" (0.000041)
1634. feature "L3MBTL3" (0.000041)
1635. feature "UNC50" (0.000040)
1636. feature "DOHH" (0.000040)
1637. feature "WDR24" (0.000040)
1638. feature "SYT12" (0.000040)
1639. feature "ZNF704" (0.000040)
1640. feature "GATA2" (0.000040)
1641. feature "SP2" (0.000040)
1642. feature "RAB3B" (0.000040)
1643. feature "TMEM94" (0.000040)
1644. feature "LINC00475" (0.000040)
1645. feature "KHNYN" (0.000040)
1646. feature "ANKRD54" (0.000039)
1647. feature "DTX3" (0.000039)
1648. feature "FRAT2" (0.000039)
1649. feature "LIPE-AS1" (0.000039)
1650. feature "MPC1-DT" (0.000039)
1651. feature "ANKRD13D" (0.000039)
1652. feature "TMEM218" (0.000039)
1653. feature "LRRC37A3" (0.000039)
1654. feature "ZNF8" (0.000039)
1655. feature "CRTC1" (0.000039)
1656. feature "NAPRT" (0.000039)
1657. feature "TRAPPC9" (0.000039)
1658. feature "SH3D19" (0.000038)
1659. feature "CEP68" (0.000038)
1660. feature "ALKBH8" (0.000038)
1661. feature "ABCD4" (0.000038)
1662. feature "PHLDB2" (0.000038)
1663. feature "MYLK-AS1" (0.000038)
1664. feature "CCNE1" (0.000038)
1665. feature "XRRA1" (0.000038)
1666. feature "FSBP" (0.000038)
1667. feature "ATP1A1-AS1" (0.000038)
1668. feature "HSF2BP" (0.000038)
1669. feature "PLIN5" (0.000038)
1670. feature "GFOD1" (0.000038)
1671. feature "KITLG" (0.000038)
1672. feature "MT-TS2" (0.000038)
1673. feature "S100A9" (0.000038)
1674. feature "BBOF1" (0.000038)
1675. feature "IPO5P1" (0.000038)
1676. feature "FBXO32" (0.000038)
1677. feature "HSD17B2" (0.000038)
1678. feature "RUNDC1" (0.000038)
1679. feature "CATSPER2" (0.000038)
1680. feature "CEP85" (0.000037)
1681. feature "ECHDC2" (0.000037)
1682. feature "PKP1" (0.000037)
1683. feature "RPUSD4" (0.000037)
1684. feature "ZNF257" (0.000037)
1685. feature "ATP6V0D1-DT" (0.000037)
1686. feature "PTPN21" (0.000037)
1687. feature "LYPD4" (0.000037)
1688. feature "S1PR2" (0.000037)
1689. feature "ZNF317" (0.000037)
1690. feature "TTYH1" (0.000037)
1691. feature "KRT13" (0.000037)
1692. feature "NLRC3" (0.000037)
1693. feature "TGM2" (0.000037)
1694. feature "R3HDM4" (0.000037)
1695. feature "LRFN4" (0.000037)
1696. feature "NOVA1" (0.000037)
1697. feature "ETAA1" (0.000037)
1698. feature "BET1L" (0.000037)
1699. feature "GCLM" (0.000036)
1700. feature "ITGB6" (0.000036)
1701. feature "HBEGF" (0.000036)
1702. feature "ARHGEF17" (0.000036)
1703. feature "BMPR1A" (0.000036)
1704. feature "CABLES1" (0.000036)
1705. feature "SNX8" (0.000036)
1706. feature "NADK" (0.000036)
1707. feature "CTNNA1-AS1" (0.000036)
1708. feature "PATZ1" (0.000036)
1709. feature "PEF1" (0.000036)
1710. feature "RCC1" (0.000036)
1711. feature "TBP" (0.000036)
1712. feature "GRAMD4P3" (0.000036)
1713. feature "PARVB" (0.000036)
1714. feature "NUDT17" (0.000036)
1715. feature "BICRAL" (0.000036)
1716. feature "TMEM63C" (0.000036)
1717. feature "CCDC69" (0.000036)
1718. feature "SNHG20" (0.000036)
1719. feature "GSDMB" (0.000036)
1720. feature "CCNY-AS1" (0.000036)
1721. feature "DDX58" (0.000036)
1722. feature "IRS3P" (0.000035)
1723. feature "SESN2" (0.000035)
1724. feature "TTYH3" (0.000035)
1725. feature "RIPOR2" (0.000035)
1726. feature "IL1RAP" (0.000035)
1727. feature "FBXO30" (0.000035)
1728. feature "DIO2" (0.000035)
1729. feature "MARCHF1" (0.000035)
1730. feature "CCNYL1" (0.000035)
1731. feature "GMIP" (0.000035)
1732. feature "EHHADH" (0.000035)
1733. feature "CLEC2D" (0.000035)
1734. feature "RAB37" (0.000035)
1735. feature "KIF18B" (0.000035)
1736. feature "CLDN15" (0.000035)
1737. feature "FCHO1" (0.000035)
1738. feature "ABCB9" (0.000035)
1739. feature "EHD1" (0.000035)
1740. feature "GRASLND" (0.000035)
1741. feature "PYY2" (0.000035)
1742. feature "DNAJC6" (0.000035)
1743. feature "ABT1" (0.000035)
1744. feature "PCDHGA10" (0.000035)
1745. feature "TBC1D4" (0.000035)
1746. feature "CLDN11" (0.000035)
1747. feature "NSMF" (0.000035)
1748. feature "SBF1" (0.000035)
1749. feature "C5" (0.000035)
1750. feature "BAHD1" (0.000035)
1751. feature "SENP3" (0.000035)
1752. feature "ZNF79" (0.000034)
1753. feature "E2F1" (0.000034)
1754. feature "MEX3D" (0.000034)
1755. feature "PCDHA10" (0.000034)
1756. feature "NAIF1" (0.000034)
1757. feature "ZBTB45" (0.000034)
1758. feature "EFCAB2" (0.000034)
1759. feature "C6orf132" (0.000034)
1760. feature "PLA2G15" (0.000034)
1761. feature "CCDC18-AS1" (0.000034)
1762. feature "MAFK" (0.000034)
1763. feature "ZNF442" (0.000034)
1764. feature "UNC119" (0.000034)
1765. feature "ZFYVE28" (0.000034)
1766. feature "SLC66A2" (0.000034)
1767. feature "LINC01232" (0.000034)
1768. feature "ERVK3-1" (0.000034)
1769. feature "GTF2IRD1" (0.000034)
1770. feature "DNAJC28" (0.000034)
1771. feature "CCDC51" (0.000034)
1772. feature "CIC" (0.000034)
1773. feature "ARNTL2" (0.000034)
1774. feature "OR2C3" (0.000034)
1775. feature "PELI1" (0.000034)
1776. feature "NAGLU" (0.000034)
1777. feature "VCAN" (0.000034)
1778. feature "MICAL2" (0.000034)
1779. feature "IFI27" (0.000034)
1780. feature "SETD9" (0.000034)
1781. feature "SVIL-AS1" (0.000034)
1782. feature "WFDC1" (0.000034)
1783. feature "ZSWIM5" (0.000034)
1784. feature "CACNB2" (0.000034)
1785. feature "RENO1" (0.000034)
1786. feature "MHENCR" (0.000034)
1787. feature "EMX2OS" (0.000033)
1788. feature "MAN1C1" (0.000033)
1789. feature "DOK7" (0.000033)
1790. feature "ZBED5-AS1" (0.000033)
1791. feature "PINK1-AS" (0.000033)
1792. feature "SCNN1A" (0.000033)
1793. feature "RABGGTB" (0.000033)
1794. feature "CNR2" (0.000033)
1795. feature "LBHD1" (0.000033)
1796. feature "KIAA0825" (0.000033)
1797. feature "LINC01488" (0.000033)
1798. feature "FBXL4" (0.000033)
1799. feature "MEGF6" (0.000033)
1800. feature "TRAPPC13" (0.000033)
1801. feature "DCLRE1A" (0.000033)
1802. feature "OR2AT4" (0.000033)
1803. feature "MTMR1" (0.000033)
1804. feature "CDKL1" (0.000033)
1805. feature "GCAT" (0.000033)
1806. feature "ST3GAL1-DT" (0.000033)
1807. feature "ZFP82" (0.000033)
1808. feature "BATF" (0.000033)
1809. feature "STK35" (0.000033)
1810. feature "ADGRF1" (0.000033)
1811. feature "CAPRIN2" (0.000033)
1812. feature "TRMT2A" (0.000033)
1813. feature "TWNK" (0.000033)
1814. feature "ZIC1" (0.000032)
1815. feature "SLC25A19" (0.000032)
1816. feature "MT-TC" (0.000032)
1817. feature "GASAL1" (0.000032)
1818. feature "FAM86EP" (0.000032)
1819. feature "NXN" (0.000032)
1820. feature "MAP2K5" (0.000032)
1821. feature "LINC01750" (0.000032)
1822. feature "ALDH2" (0.000032)
1823. feature "DOC2A" (0.000032)
1824. feature "ABCA1" (0.000032)
1825. feature "ZNF626" (0.000032)
1826. feature "WDFY3-AS2" (0.000032)
1827. feature "PLEKHN1" (0.000032)
1828. feature "DLGAP1-AS2" (0.000032)
1829. feature "TVP23C" (0.000032)
1830. feature "RALGPS1" (0.000032)
1831. feature "POLB" (0.000032)
1832. feature "BCDIN3D" (0.000032)
1833. feature "SLC27A4" (0.000032)
1834. feature "KIF26A" (0.000032)
1835. feature "FAM136BP" (0.000032)
1836. feature "DGKQ" (0.000032)
1837. feature "TRPV3" (0.000032)
1838. feature "KAT2B" (0.000032)
1839. feature "SORBS3" (0.000032)
1840. feature "TUBE1" (0.000031)
1841. feature "MUL1" (0.000031)
1842. feature "C5AR1" (0.000031)
1843. feature "ITGB8-AS1" (0.000031)
1844. feature "EDA" (0.000031)
1845. feature "HSPD1P11" (0.000031)
1846. feature "THBS3" (0.000031)
1847. feature "SNHG12" (0.000031)
1848. feature "PAPOLA-DT" (0.000031)
1849. feature "DNAJA4" (0.000031)
1850. feature "OR5AS1" (0.000031)
1851. feature "KRTAP5-AS1" (0.000031)
1852. feature "UST" (0.000031)
1853. feature "SRCAP" (0.000031)
1854. feature "ZNF329" (0.000031)
1855. feature "RNF44" (0.000031)
1856. feature "TSHZ1" (0.000031)
1857. feature "AGAP2-AS1" (0.000031)
1858. feature "CROCCP3" (0.000031)
1859. feature "KANK1" (0.000031)
1860. feature "TSPAN33" (0.000031)
1861. feature "B4GALT1-AS1" (0.000031)
1862. feature "IKBKG" (0.000031)
1863. feature "TPCN2" (0.000031)
1864. feature "ZNF304" (0.000031)
1865. feature "TRPT1" (0.000031)
1866. feature "ZNF384" (0.000031)
1867. feature "CXADR" (0.000031)
1868. feature "VPS52" (0.000031)
1869. feature "CPEB4" (0.000031)
1870. feature "WDR72" (0.000030)
1871. feature "RAB40C" (0.000030)
1872. feature "SKIDA1" (0.000030)
1873. feature "GTPBP2" (0.000030)
1874. feature "LRRC69" (0.000030)
1875. feature "HDHD5-AS1" (0.000030)
1876. feature "ARVCF" (0.000030)
1877. feature "AMH" (0.000030)
1878. feature "DNAAF11" (0.000030)
1879. feature "SULT1B1" (0.000030)
1880. feature "HELZ2" (0.000030)
1881. feature "PURPL" (0.000030)
1882. feature "RPL36AP26" (0.000030)
1883. feature "OAZ3" (0.000030)
1884. feature "B3GAT2" (0.000030)
1885. feature "CALML3-AS1" (0.000030)
1886. feature "ALDH3B2" (0.000030)
1887. feature "LIPT1" (0.000030)
1888. feature "MTF1" (0.000030)
1889. feature "SOWAHC" (0.000030)
1890. feature "TM4SF19-AS1" (0.000030)
1891. feature "GPATCH3" (0.000030)
1892. feature "ILDR1" (0.000030)
1893. feature "MAD2L1BP" (0.000030)
1894. feature "LRRC73" (0.000030)
1895. feature "STARD3" (0.000030)
1896. feature "C2CD2" (0.000030)
1897. feature "CCDC88B" (0.000030)
1898. feature "CRTC2" (0.000030)
1899. feature "PYCR1" (0.000030)
1900. feature "TSBP1-AS1" (0.000030)
1901. feature "LINC00639" (0.000030)
1902. feature "STAM-DT" (0.000029)
1903. feature "NCK2" (0.000029)
1904. feature "RNF146" (0.000029)
1905. feature "UCKL1" (0.000029)
1906. feature "ST6GAL1" (0.000029)
1907. feature "EDA2R" (0.000029)
1908. feature "HR" (0.000029)
1909. feature "DOLPP1" (0.000029)
1910. feature "PCGF2" (0.000029)
1911. feature "HMGB1P39" (0.000029)
1912. feature "STRIP1" (0.000029)
1913. feature "PPP2R3B" (0.000029)
1914. feature "FRAT1" (0.000029)
1915. feature "CNIH3" (0.000029)
1916. feature "TENT5C" (0.000029)
1917. feature "TMEM212" (0.000029)
1918. feature "NTN1" (0.000029)
1919. feature "PEX7" (0.000029)
1920. feature "IL4R" (0.000029)
1921. feature "RBM15B" (0.000029)
1922. feature "RPS12P16" (0.000029)
1923. feature "SERPINB9P1" (0.000029)
1924. feature "TRIM3" (0.000029)
1925. feature "SLC23A2" (0.000029)
1926. feature "TRAF4" (0.000029)
1927. feature "CCND3" (0.000029)
1928. feature "SNHG30" (0.000029)
1929. feature "ARRDC2" (0.000029)
1930. feature "FAM20C" (0.000029)
1931. feature "BORCS6" (0.000029)
1932. feature "AFAP1L2" (0.000029)
1933. feature "RAET1E" (0.000029)
1934. feature "ZDHHC18" (0.000029)
1935. feature "ELOVL7" (0.000029)
1936. feature "SPSB1" (0.000029)
1937. feature "CAPN13" (0.000028)
1938. feature "ZNF696" (0.000028)
1939. feature "GPRIN1" (0.000028)
1940. feature "RBM24" (0.000028)
1941. feature "DIABLO" (0.000028)
1942. feature "OR7E25P" (0.000028)
1943. feature "LIX1L" (0.000028)
1944. feature "PCDHB1" (0.000028)
1945. feature "MAPK8IP2" (0.000028)
1946. feature "MT1F" (0.000028)
1947. feature "ACTG2" (0.000028)
1948. feature "ZNF383" (0.000028)
1949. feature "MRPL38" (0.000028)
1950. feature "NCAM1" (0.000028)
1951. feature "SLC25A43" (0.000028)
1952. feature "BRME1" (0.000028)
1953. feature "NCDN" (0.000028)
1954. feature "TMEM167B-DT" (0.000028)
1955. feature "KIFC2" (0.000028)
1956. feature "SCARA3" (0.000028)
1957. feature "DDX28" (0.000028)
1958. feature "MRTFA" (0.000028)
1959. feature "KIZ" (0.000028)
1960. feature "FAM241B" (0.000028)
1961. feature "FAM219B" (0.000028)
1962. feature "LYPD6" (0.000028)
1963. feature "ZNF165" (0.000028)
1964. feature "PELI3" (0.000028)
1965. feature "FBXO27" (0.000028)
1966. feature "FHIP1A" (0.000028)
1967. feature "FZD1" (0.000028)
1968. feature "AUNIP" (0.000028)
1969. feature "SLC19A1" (0.000028)
1970. feature "RRAGC" (0.000027)
1971. feature "PPP3CC" (0.000027)
1972. feature "ANKRD20A5P" (0.000027)
1973. feature "MMP15" (0.000027)
1974. feature "MAFG" (0.000027)
1975. feature "CRYBG2" (0.000027)
1976. feature "CCDC144A" (0.000027)
1977. feature "LINC01234" (0.000027)
1978. feature "STAT5B" (0.000027)
1979. feature "ZNF219" (0.000027)
1980. feature "CDK9" (0.000027)
1981. feature "PC" (0.000027)
1982. feature "ABHD15" (0.000027)
1983. feature "STON2" (0.000027)
1984. feature "MID2" (0.000027)
1985. feature "TAF8" (0.000027)
1986. feature "RBFOX3" (0.000027)
1987. feature "U2AF1L4" (0.000027)
1988. feature "SNORD3B-1" (0.000027)
1989. feature "PLPPR2" (0.000027)
1990. feature "ZNF449" (0.000027)
1991. feature "BCAM" (0.000027)
1992. feature "POTEC" (0.000027)
1993. feature "ZNF628" (0.000027)
1994. feature "TBC1D22B" (0.000027)
1995. feature "NRARP" (0.000026)
1996. feature "MAP3K14-AS1" (0.000026)
1997. feature "ZKSCAN8" (0.000026)
1998. feature "ADCK1" (0.000026)
1999. feature "ELMOD3" (0.000026)
2000. feature "RASSF8" (0.000026)
2001. feature "PABIR2" (0.000026)
2002. feature "HECA" (0.000026)
2003. feature "MARS2" (0.000026)
2004. feature "FAM110D" (0.000026)
2005. feature "SLC35C1" (0.000026)
2006. feature "TRIM39" (0.000026)
2007. feature "ZBTB26" (0.000026)
2008. feature "PCDHB4" (0.000026)
2009. feature "TRIT1" (0.000026)
2010. feature "BDNF-AS" (0.000026)
2011. feature "TMEM115" (0.000026)
2012. feature "VPS18" (0.000026)
2013. feature "C8orf58" (0.000026)
2014. feature "GCAWKR" (0.000026)
2015. feature "PLEKHG6" (0.000026)
2016. feature "PRR7-AS1" (0.000026)
2017. feature "OR10K1" (0.000026)
2018. feature "SCRG1" (0.000026)
2019. feature "LINC00345" (0.000026)
2020. feature "DLX1" (0.000026)
2021. feature "GASK1B-AS1" (0.000026)
2022. feature "SNAP25-AS1" (0.000026)
2023. feature "N6AMT1" (0.000026)
2024. feature "ZNF354B" (0.000026)
2025. feature "FHIP1B" (0.000026)
2026. feature "MTATP6P1" (0.000026)
2027. feature "RRP9" (0.000026)
2028. feature "TWF2" (0.000026)
2029. feature "LINC01918" (0.000025)
2030. feature "TEAD2" (0.000025)
2031. feature "PCSK5" (0.000025)
2032. feature "UBIAD1" (0.000025)
2033. feature "ZNF77" (0.000025)
2034. feature "KPNB1P1" (0.000025)
2035. feature "PPARA" (0.000025)
2036. feature "ITGB8" (0.000025)
2037. feature "BCR" (0.000025)
2038. feature "ZNF524" (0.000025)
2039. feature "SALRNA1" (0.000025)
2040. feature "GAB2" (0.000025)
2041. feature "POMK" (0.000025)
2042. feature "ADAP1" (0.000025)
2043. feature "NANP" (0.000025)
2044. feature "MYO1F" (0.000025)
2045. feature "HCG11" (0.000025)
2046. feature "PADI2" (0.000025)
2047. feature "PIP5K1C" (0.000025)
2048. feature "RSAD1" (0.000025)
2049. feature "LINC01206" (0.000025)
2050. feature "TADA2B" (0.000025)
2051. feature "PARM1" (0.000025)
2052. feature "IL1R1" (0.000025)
2053. feature "THRA" (0.000025)
2054. feature "NOP2" (0.000025)
2055. feature "CDKN2B" (0.000025)
2056. feature "CNPY4" (0.000025)
2057. feature "PRPF40B" (0.000025)
2058. feature "BFSP1" (0.000025)
2059. feature "METTL14-DT" (0.000025)
2060. feature "OLFM2" (0.000025)
2061. feature "RHD" (0.000025)
2062. feature "OSBP2" (0.000025)
2063. feature "AQP11" (0.000025)
2064. feature "CRB1" (0.000024)
2065. feature "CEACAM5" (0.000024)
2066. feature "CREB3L1" (0.000024)
2067. feature "TRIM8" (0.000024)
2068. feature "SH3PXD2A" (0.000024)
2069. feature "ADD2" (0.000024)
2070. feature "CABLES2" (0.000024)
2071. feature "PPP1R12A-AS1" (0.000024)
2072. feature "MFSD4A" (0.000024)
2073. feature "MMD" (0.000024)
2074. feature "ZNF572" (0.000024)
2075. feature "ARHGAP39" (0.000024)
2076. feature "CHAC2" (0.000024)
2077. feature "FUOM" (0.000024)
2078. feature "KRT16" (0.000024)
2079. feature "WFS1" (0.000024)
2080. feature "GRIN1" (0.000024)
2081. feature "LINC00343" (0.000024)
2082. feature "ZNF396" (0.000024)
2083. feature "EPCAM-DT" (0.000024)
2084. feature "INPP5E" (0.000024)
2085. feature "EFCAB5" (0.000024)
2086. feature "SNAPC2" (0.000024)
2087. feature "ZKSCAN2" (0.000024)
2088. feature "ZNF550" (0.000024)
2089. feature "CDC42EP1" (0.000024)
2090. feature "CCDC88A" (0.000024)
2091. feature "HECTD2" (0.000024)
2092. feature "TBXA2R" (0.000024)
2093. feature "POC1A" (0.000024)
2094. feature "CCDC183" (0.000024)
2095. feature "ANKRD45" (0.000024)
2096. feature "TRIM68" (0.000024)
2097. feature "RHOU" (0.000024)
2098. feature "ZBED6CL" (0.000024)
2099. feature "SIGMAR1" (0.000024)
2100. feature "TNNC2" (0.000024)
2101. feature "ABHD1" (0.000024)
2102. feature "ZNF202" (0.000024)
2103. feature "CDADC1" (0.000023)
2104. feature "SLC2A13" (0.000023)
2105. feature "FAM185BP" (0.000023)
2106. feature "RHOH" (0.000023)
2107. feature "MIB2" (0.000023)
2108. feature "ESS2" (0.000023)
2109. feature "CASP10" (0.000023)
2110. feature "LY6D" (0.000023)
2111. feature "IGSF6" (0.000023)
2112. feature "LINC02482" (0.000023)
2113. feature "ZNF280B" (0.000023)
2114. feature "ARHGEF4" (0.000023)
2115. feature "PPP2R2B" (0.000023)
2116. feature "TENT2" (0.000023)
2117. feature "MAPT-AS1" (0.000023)
2118. feature "MEMO1" (0.000023)
2119. feature "PLXNA1" (0.000023)
2120. feature "HEATR3" (0.000023)
2121. feature "LINC02609" (0.000023)
2122. feature "PARP16" (0.000023)
2123. feature "TDRD5" (0.000023)
2124. feature "ZNF843" (0.000023)
2125. feature "SCX" (0.000023)
2126. feature "STK16" (0.000023)
2127. feature "TMEM201" (0.000023)
2128. feature "PLAC9P1" (0.000023)
2129. feature "WASH3P" (0.000023)
2130. feature "REM2" (0.000023)
2131. feature "MKLN1-AS" (0.000023)
2132. feature "CCDC120" (0.000023)
2133. feature "LINC01128" (0.000023)
2134. feature "GPR137C" (0.000023)
2135. feature "DISP1" (0.000023)
2136. feature "ADAMTSL5" (0.000023)
2137. feature "ZNF180" (0.000022)
2138. feature "IFITM3P6" (0.000022)
2139. feature "TCEANC" (0.000022)
2140. feature "ARMC6" (0.000022)
2141. feature "CCNO" (0.000022)
2142. feature "AKT3" (0.000022)
2143. feature "SMIM5" (0.000022)
2144. feature "ABHD16A" (0.000022)
2145. feature "LINC02241" (0.000022)
2146. feature "NLE1" (0.000022)
2147. feature "WRAP73" (0.000022)
2148. feature "LINC01962" (0.000022)
2149. feature "ABCD1" (0.000022)
2150. feature "LINC01725" (0.000022)
2151. feature "GALM" (0.000022)
2152. feature "STRIP2" (0.000022)
2153. feature "ZNF653" (0.000022)
2154. feature "ZNF816" (0.000022)
2155. feature "LINC02762" (0.000022)
2156. feature "ARAP1" (0.000022)
2157. feature "FAM43A" (0.000022)
2158. feature "LINC00958" (0.000022)
2159. feature "MT-TM" (0.000022)
2160. feature "SLC9A3-AS1" (0.000022)
2161. feature "MTCL1" (0.000022)
2162. feature "SLC47A1" (0.000022)
2163. feature "PTDSS2" (0.000022)
2164. feature "TMPRSS3" (0.000022)
2165. feature "TMEM139" (0.000022)
2166. feature "WDR5B-DT" (0.000022)
2167. feature "CDC14A" (0.000022)
2168. feature "EPOP" (0.000022)
2169. feature "PLCD4" (0.000022)
2170. feature "ST6GALNAC4" (0.000022)
2171. feature "C6orf136" (0.000022)
2172. feature "SLC19A2" (0.000022)
2173. feature "PCAT1" (0.000022)
2174. feature "UAP1L1" (0.000022)
2175. feature "RRN3P1" (0.000022)
2176. feature "DFFB" (0.000022)
2177. feature "DDX25" (0.000022)
2178. feature "FOXP4-AS1" (0.000022)
2179. feature "VAV1" (0.000022)
2180. feature "RHOBTB2" (0.000022)
2181. feature "NEK8" (0.000022)
2182. feature "GRM6" (0.000022)
2183. feature "USF1" (0.000021)
2184. feature "GNAO1-DT" (0.000021)
2185. feature "KLHL22" (0.000021)
2186. feature "DNAJC3-DT" (0.000021)
2187. feature "PIKFYVE" (0.000021)
2188. feature "ANKRD13B" (0.000021)
2189. feature "TBKBP1" (0.000021)
2190. feature "ADCY2" (0.000021)
2191. feature "GAMT" (0.000021)
2192. feature "LHX2" (0.000021)
2193. feature "PIDD1" (0.000021)
2194. feature "GNG7" (0.000021)
2195. feature "CYB561D2" (0.000021)
2196. feature "CACNA1H" (0.000021)
2197. feature "ST20-AS1" (0.000021)
2198. feature "HYAL2" (0.000021)
2199. feature "OXSM" (0.000021)
2200. feature "NOSTRIN" (0.000021)
2201. feature "PCBP3" (0.000021)
2202. feature "GPR137B" (0.000021)
2203. feature "TANGO2" (0.000021)
2204. feature "HMGB1P8" (0.000021)
2205. feature "HECTD3" (0.000021)
2206. feature "ZBTB39" (0.000021)
2207. feature "PPP1R18" (0.000021)
2208. feature "CLCF1" (0.000021)
2209. feature "ZNF578" (0.000021)
2210. feature "TPST1" (0.000021)
2211. feature "DOCK9" (0.000021)
2212. feature "B3GALT5" (0.000021)
2213. feature "MFSD2B" (0.000021)
2214. feature "PTGES3L" (0.000021)
2215. feature "CERK" (0.000021)
2216. feature "RNF126" (0.000021)
2217. feature "ANKRD34A" (0.000021)
2218. feature "ZC3H12A" (0.000021)
2219. feature "CDAN1" (0.000020)
2220. feature "GPAT3" (0.000020)
2221. feature "RYR3" (0.000020)
2222. feature "ZNF579" (0.000020)
2223. feature "NRG1" (0.000020)
2224. feature "MPV17L2" (0.000020)
2225. feature "APOBEC3F" (0.000020)
2226. feature "HSP90AA2P" (0.000020)
2227. feature "ARL17B" (0.000020)
2228. feature "BCL2L2" (0.000020)
2229. feature "ZFP3" (0.000020)
2230. feature "DENND3" (0.000020)
2231. feature "FLT3LG" (0.000020)
2232. feature "CHRNB1" (0.000020)
2233. feature "HACD4" (0.000020)
2234. feature "TESK1" (0.000020)
2235. feature "MCOLN1" (0.000020)
2236. feature "MPP3" (0.000020)
2237. feature "GAS2L1" (0.000020)
2238. feature "KCNC3" (0.000020)
2239. feature "PRR19" (0.000020)
2240. feature "RAVER1" (0.000020)
2241. feature "C19orf73" (0.000020)
2242. feature "FBXO17" (0.000020)
2243. feature "TBC1D8" (0.000020)
2244. feature "NPFFR1" (0.000020)
2245. feature "EREG" (0.000020)
2246. feature "VASH1" (0.000020)
2247. feature "SNX25" (0.000020)
2248. feature "KIF12" (0.000020)
2249. feature "UPP1" (0.000020)
2250. feature "PITPNM2" (0.000020)
2251. feature "TNS2" (0.000020)
2252. feature "MIRLET7BHG" (0.000020)
2253. feature "GPRASP2" (0.000020)
2254. feature "ZC3H10" (0.000020)
2255. feature "GMEB2" (0.000020)
2256. feature "TNIK" (0.000020)
2257. feature "NXPE3" (0.000020)
2258. feature "TSACC" (0.000020)
2259. feature "ZNF837" (0.000020)
2260. feature "AMIGO1" (0.000020)
2261. feature "PAQR4" (0.000020)
2262. feature "ZNF433" (0.000020)
2263. feature "EPM2A" (0.000020)
2264. feature "FMNL3" (0.000019)
2265. feature "XKR6" (0.000019)
2266. feature "TMEM222" (0.000019)
2267. feature "FAM81A" (0.000019)
2268. feature "LINC01551" (0.000019)
2269. feature "PECR" (0.000019)
2270. feature "ARHGEF34P" (0.000019)
2271. feature "PYCR2" (0.000019)
2272. feature "TMX3" (0.000019)
2273. feature "YRDC" (0.000019)
2274. feature "PFKFB2" (0.000019)
2275. feature "B4GAT1" (0.000019)
2276. feature "CKMT1A" (0.000019)
2277. feature "FOSB" (0.000019)
2278. feature "TTC38" (0.000019)
2279. feature "TMEM62" (0.000019)
2280. feature "LINC00672" (0.000019)
2281. feature "MOB2" (0.000019)
2282. feature "HEXD-IT1" (0.000019)
2283. feature "MSL3P1" (0.000019)
2284. feature "EN2" (0.000019)
2285. feature "SNORC" (0.000019)
2286. feature "ZNF773" (0.000019)
2287. feature "KIAA0895" (0.000019)
2288. feature "FAM89A" (0.000019)
2289. feature "TNFAIP8L3" (0.000019)
2290. feature "DOK4" (0.000019)
2291. feature "GLMP" (0.000019)
2292. feature "GPR85" (0.000019)
2293. feature "NOG" (0.000018)
2294. feature "KLHL13" (0.000018)
2295. feature "SCN9A" (0.000018)
2296. feature "MSTO1" (0.000018)
2297. feature "DENND5B" (0.000018)
2298. feature "FAM72D" (0.000018)
2299. feature "PAX9" (0.000018)
2300. feature "MMP16" (0.000018)
2301. feature "SRRD" (0.000018)
2302. feature "SLC36A4" (0.000018)
2303. feature "RPL37P6" (0.000018)
2304. feature "NFKBID" (0.000018)
2305. feature "ARHGEF28" (0.000018)
2306. feature "SMPD2" (0.000018)
2307. feature "SPRED3" (0.000018)
2308. feature "TXNRD2" (0.000018)
2309. feature "SPATA6L" (0.000018)
2310. feature "SIX3" (0.000018)
2311. feature "CDKN2D" (0.000018)
2312. feature "CACNB4" (0.000018)
2313. feature "E2F2" (0.000018)
2314. feature "KLK6" (0.000018)
2315. feature "C12orf56" (0.000018)
2316. feature "SLC41A3-AS1" (0.000018)
2317. feature "IPPK" (0.000018)
2318. feature "CCDC81" (0.000018)
2319. feature "L3HYPDH" (0.000018)
2320. feature "FAM71D" (0.000018)
2321. feature "PARD3B" (0.000018)
2322. feature "MAPK12" (0.000018)
2323. feature "LPAR6" (0.000018)
2324. feature "SLC30A1" (0.000018)
2325. feature "NOTCH4" (0.000018)
2326. feature "TMPRSS2" (0.000018)
2327. feature "ADAP2" (0.000018)
2328. feature "JHY" (0.000018)
2329. feature "TRMT61A" (0.000018)
2330. feature "ATRNL1" (0.000018)
2331. feature "PER1" (0.000018)
2332. feature "RUNDC3A" (0.000018)
2333. feature "CERS4" (0.000018)
2334. feature "KATNB1" (0.000018)
2335. feature "GPN1" (0.000018)
2336. feature "SNORA11F" (0.000018)
2337. feature "EDC4" (0.000018)
2338. feature "PARP12" (0.000018)
2339. feature "ADM2" (0.000018)
2340. feature "PTHLH" (0.000018)
2341. feature "ATP2A1-AS1" (0.000018)
2342. feature "TFEB" (0.000018)
2343. feature "ZNF699" (0.000018)
2344. feature "S100A8" (0.000018)
2345. feature "DOCK11" (0.000018)
2346. feature "NR2F1-AS1" (0.000018)
2347. feature "ATG9A" (0.000018)
2348. feature "A4GALT" (0.000017)
2349. feature "CLYBL" (0.000017)
2350. feature "EML5" (0.000017)
2351. feature "RTL10" (0.000017)
2352. feature "MON1A" (0.000017)
2353. feature "MPP2" (0.000017)
2354. feature "ARMCX4" (0.000017)
2355. feature "MSX2" (0.000017)
2356. feature "KCNJ6" (0.000017)
2357. feature "KLK10" (0.000017)
2358. feature "GABBR2" (0.000017)
2359. feature "LRRC56" (0.000017)
2360. feature "PLEKHA4" (0.000017)
2361. feature "ZNF343" (0.000017)
2362. feature "ZNF76" (0.000017)
2363. feature "VWA2" (0.000017)
2364. feature "CXCL12" (0.000017)
2365. feature "PIGB" (0.000017)
2366. feature "DLX6-AS1" (0.000017)
2367. feature "PACC1" (0.000017)
2368. feature "TMEM39A" (0.000017)
2369. feature "LINC02324" (0.000017)
2370. feature "NFATC2" (0.000017)
2371. feature "ZNF321P" (0.000017)
2372. feature "MELTF" (0.000017)
2373. feature "INPP4A" (0.000017)
2374. feature "PHETA1" (0.000017)
2375. feature "ZIC2" (0.000017)
2376. feature "BRINP2" (0.000017)
2377. feature "ZNF672" (0.000017)
2378. feature "HHLA1" (0.000017)
2379. feature "EID2" (0.000017)
2380. feature "SLC4A10" (0.000017)
2381. feature "CYP1A1" (0.000017)
2382. feature "PLEKHG5" (0.000017)
2383. feature "DOCK8" (0.000017)
2384. feature "KIF13B" (0.000017)
2385. feature "ZNF2" (0.000017)
2386. feature "HHEX" (0.000017)
2387. feature "BCL2" (0.000017)
2388. feature "ZEB1-AS1" (0.000017)
2389. feature "NFATC3" (0.000017)
2390. feature "ZFP41" (0.000017)
2391. feature "CSGALNACT1" (0.000017)
2392. feature "ADORA1" (0.000017)
2393. feature "RFPL3S" (0.000017)
2394. feature "ZNF335" (0.000017)
2395. feature "TNFRSF10B" (0.000016)
2396. feature "PRICKLE2" (0.000016)
2397. feature "FOXO6" (0.000016)
2398. feature "INMT" (0.000016)
2399. feature "LRRC37A" (0.000016)
2400. feature "ZFHX2" (0.000016)
2401. feature "RTN4RL1" (0.000016)
2402. feature "KCTD6" (0.000016)
2403. feature "ZNF722P" (0.000016)
2404. feature "MSRB3" (0.000016)
2405. feature "GARNL3" (0.000016)
2406. feature "IGDCC3" (0.000016)
2407. feature "SUSD3" (0.000016)
2408. feature "USP46-DT" (0.000016)
2409. feature "RSPH1" (0.000016)
2410. feature "HMGB1P1" (0.000016)
2411. feature "CORO2A" (0.000016)
2412. feature "ARHGEF18" (0.000016)
2413. feature "STIMATE" (0.000016)
2414. feature "HAP1" (0.000016)
2415. feature "ZIC5" (0.000016)
2416. feature "C20orf204" (0.000016)
2417. feature "ADRA2C" (0.000016)
2418. feature "TNFAIP2" (0.000016)
2419. feature "SLC66A1" (0.000016)
2420. feature "PGBD5" (0.000016)
2421. feature "ATP6V0C" (0.000016)
2422. feature "MAMLD1" (0.000016)
2423. feature "DHRS11" (0.000016)
2424. feature "FAM222A" (0.000016)
2425. feature "SPHK1" (0.000016)
2426. feature "HPS6" (0.000016)
2427. feature "TMEM51" (0.000016)
2428. feature "CSRNP1" (0.000016)
2429. feature "AK1" (0.000016)
2430. feature "LPCAT4" (0.000016)
2431. feature "PYGO1" (0.000016)
2432. feature "FA2H" (0.000016)
2433. feature "MOCS1" (0.000016)
2434. feature "MYLPF" (0.000016)
2435. feature "MYG1-AS1" (0.000016)
2436. feature "HTR7P1" (0.000016)
2437. feature "SEC14L2" (0.000015)
2438. feature "SNHG11" (0.000015)
2439. feature "SATL1" (0.000015)
2440. feature "LRP3" (0.000015)
2441. feature "ARHGEF19" (0.000015)
2442. feature "CAPN10" (0.000015)
2443. feature "SCOC-AS1" (0.000015)
2444. feature "LIFR" (0.000015)
2445. feature "SLC29A3" (0.000015)
2446. feature "PRKCH" (0.000015)
2447. feature "ZNF585B" (0.000015)
2448. feature "ARSJ" (0.000015)
2449. feature "LINC00240" (0.000015)
2450. feature "RAB23" (0.000015)
2451. feature "ABHD3" (0.000015)
2452. feature "HELQ" (0.000015)
2453. feature "ACOT2" (0.000015)
2454. feature "RBM26-AS1" (0.000015)
2455. feature "RHOBTB1" (0.000015)
2456. feature "SIM2" (0.000015)
2457. feature "HCN2" (0.000015)
2458. feature "ZNF134" (0.000015)
2459. feature "GPR132" (0.000015)
2460. feature "SLC52A3" (0.000015)
2461. feature "ROM1" (0.000015)
2462. feature "ZNF232" (0.000015)
2463. feature "HMGN1P14" (0.000015)
2464. feature "ZNF69" (0.000015)
2465. feature "PSG2" (0.000015)
2466. feature "VDR" (0.000015)
2467. feature "CRACR2B" (0.000015)
2468. feature "TLE2" (0.000015)
2469. feature "LINC00294" (0.000015)
2470. feature "CDKL3" (0.000015)
2471. feature "RTL6" (0.000014)
2472. feature "CAMTA2" (0.000014)
2473. feature "CRX" (0.000014)
2474. feature "ALDH4A1" (0.000014)
2475. feature "LINC02913" (0.000014)
2476. feature "CHN1" (0.000014)
2477. feature "HMGB1P51" (0.000014)
2478. feature "IFIT5" (0.000014)
2479. feature "CARD18" (0.000014)
2480. feature "TRAPPC14" (0.000014)
2481. feature "RILP" (0.000014)
2482. feature "ZNF767P" (0.000014)
2483. feature "DNAI4" (0.000014)
2484. feature "RNVU1-30" (0.000014)
2485. feature "INPP1" (0.000014)
2486. feature "CHRNG" (0.000014)
2487. feature "OR2L2" (0.000014)
2488. feature "RAD52" (0.000014)
2489. feature "MAP3K10" (0.000014)
2490. feature "SIGLEC15" (0.000014)
2491. feature "PMS2CL" (0.000014)
2492. feature "MICALL1" (0.000014)
2493. feature "PCDHGB6" (0.000014)
2494. feature "ZNF674" (0.000014)
2495. feature "C2orf42" (0.000014)
2496. feature "BEX5" (0.000014)
2497. feature "C12orf76" (0.000014)
2498. feature "RPIA" (0.000014)
2499. feature "SHROOM2" (0.000014)
2500. feature "PTPN7" (0.000014)
2501. feature "KYAT3" (0.000014)
2502. feature "ITM2C" (0.000014)
2503. feature "ZNF555" (0.000014)
2504. feature "DSTNP2" (0.000014)
2505. feature "CRPPA" (0.000014)
2506. feature "LINC01426" (0.000014)
2507. feature "HS3ST3B1" (0.000013)
2508. feature "ZNF441" (0.000013)
2509. feature "RNASEK" (0.000013)
2510. feature "PRORSD1P" (0.000013)
2511. feature "PA2G4P6" (0.000013)
2512. feature "RHOQP1" (0.000013)
2513. feature "ZNF324" (0.000013)
2514. feature "KRT18P3" (0.000013)
2515. feature "ARMC7" (0.000013)
2516. feature "LRFN3" (0.000013)
2517. feature "SRP14-DT" (0.000013)
2518. feature "THAP3" (0.000013)
2519. feature "FCHSD1" (0.000013)
2520. feature "HEIH" (0.000013)
2521. feature "AKR1E2" (0.000013)
2522. feature "ZIC4" (0.000013)
2523. feature "LINC00844" (0.000013)
2524. feature "POLR2M" (0.000013)
2525. feature "SLC45A4" (0.000013)
2526. feature "SEPTIN3" (0.000013)
2527. feature "S100A2" (0.000013)
2528. feature "FAXC" (0.000013)
2529. feature "ZNF573" (0.000013)
2530. feature "KNDC1" (0.000013)
2531. feature "DPY19L1" (0.000013)
2532. feature "MYO3B-AS1" (0.000013)
2533. feature "POMGNT2" (0.000013)
2534. feature "C8orf88" (0.000013)
2535. feature "SGSH" (0.000013)
2536. feature "CARM1" (0.000013)
2537. feature "EMP3" (0.000013)
2538. feature "RFNG" (0.000013)
2539. feature "ZNF391" (0.000013)
2540. feature "CLCN7" (0.000013)
2541. feature "TRMT61B" (0.000013)
2542. feature "PCDHGA2" (0.000013)
2543. feature "DNAJC25-GNG10" (0.000013)
2544. feature "ZNF324B" (0.000013)
2545. feature "TFPI2-DT" (0.000013)
2546. feature "KCTD2" (0.000013)
2547. feature "METTL7A" (0.000013)
2548. feature "ABCG1" (0.000012)
2549. feature "ZCCHC24" (0.000012)
2550. feature "ZNF112" (0.000012)
2551. feature "C19orf47" (0.000012)
2552. feature "PCDHGC3" (0.000012)
2553. feature "MAP3K12" (0.000012)
2554. feature "KCNE4" (0.000012)
2555. feature "SUCLA2-AS1" (0.000012)
2556. feature "MAP3K14" (0.000012)
2557. feature "CTU2" (0.000012)
2558. feature "RAD54L" (0.000012)
2559. feature "HAUS5" (0.000012)
2560. feature "SGMS1-AS1" (0.000012)
2561. feature "ABTB2" (0.000012)
2562. feature "JPH3" (0.000012)
2563. feature "SLC46A1" (0.000012)
2564. feature "TCL6" (0.000012)
2565. feature "CCNT2-AS1" (0.000012)
2566. feature "ALDH3A1" (0.000012)
2567. feature "LEF1" (0.000012)
2568. feature "ZBTB22" (0.000012)
2569. feature "FCRL5" (0.000012)
2570. feature "MAPK7" (0.000012)
2571. feature "SLC26A8" (0.000012)
2572. feature "TRAK1" (0.000012)
2573. feature "CCDC71L" (0.000012)
2574. feature "ARHGEF2" (0.000012)
2575. feature "PLEKHG1" (0.000012)
2576. feature "OR5M2P" (0.000012)
2577. feature "BMS1P22" (0.000012)
2578. feature "HMGN1P17" (0.000012)
2579. feature "TMEM104" (0.000012)
2580. feature "OSBPL5" (0.000012)
2581. feature "CNIH2" (0.000012)
2582. feature "IRF7" (0.000012)
2583. feature "SMPD4BP" (0.000012)
2584. feature "BICC1" (0.000012)
2585. feature "ABCC4" (0.000012)
2586. feature "OR10H1" (0.000012)
2587. feature "SARS2" (0.000012)
2588. feature "SLC24A2" (0.000012)
2589. feature "TPTEP2" (0.000012)
2590. feature "PCED1A" (0.000012)
2591. feature "SLC25A12" (0.000012)
2592. feature "CLCN6" (0.000012)
2593. feature "UNC5B" (0.000012)
2594. feature "CALHM2" (0.000012)
2595. feature "MT-TH" (0.000011)
2596. feature "ZNF862" (0.000011)
2597. feature "ULK3" (0.000011)
2598. feature "CMTM3" (0.000011)
2599. feature "FAM118B" (0.000011)
2600. feature "BRWD1-AS2" (0.000011)
2601. feature "SOX7" (0.000011)
2602. feature "RCOR2" (0.000011)
2603. feature "MIR3677HG" (0.000011)
2604. feature "RNF180" (0.000011)
2605. feature "TCF7" (0.000011)
2606. feature "RNU6-450P" (0.000011)
2607. feature "LINC02453" (0.000011)
2608. feature "LINC00598" (0.000011)
2609. feature "CLSTN3" (0.000011)
2610. feature "PCAT19" (0.000011)
2611. feature "ZNF597" (0.000011)
2612. feature "GLB1L" (0.000011)
2613. feature "EML6" (0.000011)
2614. feature "LINC01016" (0.000011)
2615. feature "MT-TL1" (0.000011)
2616. feature "ANKRD36BP2" (0.000011)
2617. feature "TUBG2" (0.000011)
2618. feature "LINC02605" (0.000011)
2619. feature "TMEM147-AS1" (0.000011)
2620. feature "DCLK1" (0.000011)
2621. feature "LINC00667" (0.000011)
2622. feature "SIMC1" (0.000011)
2623. feature "KIF9-AS1" (0.000011)
2624. feature "IGF2BP2" (0.000011)
2625. feature "KCNV1" (0.000011)
2626. feature "ATG16L2" (0.000011)
2627. feature "CAMK2B" (0.000011)
2628. feature "MPPED2" (0.000011)
2629. feature "ZNF446" (0.000011)
2630. feature "RBMS3" (0.000011)
2631. feature "IPO13" (0.000011)
2632. feature "CHMP6" (0.000011)
2633. feature "ZNF319" (0.000011)
2634. feature "SLC25A21-AS1" (0.000011)
2635. feature "ASH1L-AS1" (0.000011)
2636. feature "RPL23AP53" (0.000011)
2637. feature "NSRP1P1" (0.000011)
2638. feature "TAP1" (0.000011)
2639. feature "LRRC20" (0.000011)
2640. feature "FN3K" (0.000010)
2641. feature "CYP4B1" (0.000010)
2642. feature "OR2I1P" (0.000010)
2643. feature "KLRG2" (0.000010)
2644. feature "C12orf50" (0.000010)
2645. feature "C1orf112" (0.000010)
2646. feature "FOSL1" (0.000010)
2647. feature "PRRT2" (0.000010)
2648. feature "VPS37D" (0.000010)
2649. feature "UBE2L6" (0.000010)
2650. feature "VASN" (0.000010)
2651. feature "FJX1" (0.000010)
2652. feature "PANK4" (0.000010)
2653. feature "PRAG1" (0.000010)
2654. feature "LINC01293" (0.000010)
2655. feature "ZNF530" (0.000010)
2656. feature "VMAC" (0.000010)
2657. feature "MOGAT3" (0.000010)
2658. feature "RPL13P2" (0.000010)
2659. feature "TRIM66" (0.000010)
2660. feature "PINK1" (0.000010)
2661. feature "DNM3" (0.000010)
2662. feature "PACSIN1" (0.000010)
2663. feature "VDAC1P8" (0.000010)
2664. feature "MAPK8IP1" (0.000010)
2665. feature "OR7E122P" (0.000010)
2666. feature "TMCC3" (0.000010)
2667. feature "H2AC10P" (0.000010)
2668. feature "RPL12P25" (0.000010)
2669. feature "OGN" (0.000010)
2670. feature "VAX2" (0.000010)
2671. feature "NFKBIZ" (0.000010)
2672. feature "KATNAL2" (0.000010)
2673. feature "PPOX" (0.000010)
2674. feature "PDE4A" (0.000010)
2675. feature "ZNF772" (0.000010)
2676. feature "ADRB1" (0.000010)
2677. feature "DLX3" (0.000010)
2678. feature "DLG5-AS1" (0.000010)
2679. feature "NOMO3" (0.000010)
2680. feature "CTNS" (0.000010)
2681. feature "NDUFV2-AS1" (0.000010)
2682. feature "VPS13B-DT" (0.000010)
2683. feature "CORO1A" (0.000010)
2684. feature "SHISA2" (0.000010)
2685. feature "SLC25A42" (0.000010)
2686. feature "CEP72" (0.000010)
2687. feature "NES" (0.000010)
2688. feature "STRIT1" (0.000010)
2689. feature "LINC01907" (0.000009)
2690. feature "B3GNT6" (0.000009)
2691. feature "THNSL2" (0.000009)
2692. feature "TRPC3" (0.000009)
2693. feature "SNHG18" (0.000009)
2694. feature "TRNP1" (0.000009)
2695. feature "GNRHR2" (0.000009)
2696. feature "CDHR3" (0.000009)
2697. feature "PKN3" (0.000009)
2698. feature "HNF4A" (0.000009)
2699. feature "ZNF799" (0.000009)
2700. feature "GAS7" (0.000009)
2701. feature "HPSE" (0.000009)
2702. feature "CTSK" (0.000009)
2703. feature "SERPINB9" (0.000009)
2704. feature "SIPA1" (0.000009)
2705. feature "KIAA0753" (0.000009)
2706. feature "HARBI1" (0.000009)
2707. feature "MYO3B" (0.000009)
2708. feature "DPPA4" (0.000009)
2709. feature "CCT6B" (0.000009)
2710. feature "CDC42EP3" (0.000009)
2711. feature "TBXAS1" (0.000009)
2712. feature "SREK1IP1P1" (0.000009)
2713. feature "PKN2-AS1" (0.000009)
2714. feature "FBXO6" (0.000009)
2715. feature "CALR4P" (0.000009)
2716. feature "NDUFA6-DT" (0.000009)
2717. feature "CYP19A1" (0.000009)
2718. feature "CARF" (0.000009)
2719. feature "CCDC61" (0.000008)
2720. feature "RAMP3" (0.000008)
2721. feature "LENG8-AS1" (0.000008)
2722. feature "STX1A" (0.000008)
2723. feature "ANKMY1" (0.000008)
2724. feature "TUT4" (0.000008)
2725. feature "GCNA" (0.000008)
2726. feature "RPL34P1" (0.000008)
2727. feature "PDCD6IPP2" (0.000008)
2728. feature "CTHRC1" (0.000008)
2729. feature "POLM" (0.000008)
2730. feature "PCDHGA1" (0.000008)
2731. feature "ERO1B" (0.000008)
2732. feature "B3GNT7" (0.000008)
2733. feature "CPLX1" (0.000008)
2734. feature "SYT16" (0.000008)
2735. feature "RAPGEF5" (0.000008)
2736. feature "C1orf210" (0.000008)
2737. feature "NXPH4" (0.000008)
2738. feature "LINC01963" (0.000008)
2739. feature "ZNF81" (0.000008)
2740. feature "SLC25A22" (0.000008)
2741. feature "GYG2" (0.000008)
2742. feature "RNF207" (0.000008)
2743. feature "ELAC1" (0.000008)
2744. feature "PODNL1" (0.000008)
2745. feature "NLGN2" (0.000008)
2746. feature "DLG4" (0.000008)
2747. feature "TRIM69" (0.000008)
2748. feature "SLC22A23" (0.000008)
2749. feature "SERTAD2" (0.000008)
2750. feature "PAX5" (0.000008)
2751. feature "C3orf35" (0.000008)
2752. feature "LINC01427" (0.000008)
2753. feature "RFFL" (0.000008)
2754. feature "PLAGL1" (0.000008)
2755. feature "PRELID3A" (0.000007)
2756. feature "LINC00853" (0.000007)
2757. feature "CTIF" (0.000007)
2758. feature "OSGEPL1" (0.000007)
2759. feature "BEND3" (0.000007)
2760. feature "ESRRA" (0.000007)
2761. feature "XKR4" (0.000007)
2762. feature "BICRA" (0.000007)
2763. feature "RASAL1" (0.000007)
2764. feature "UCHL1" (0.000007)
2765. feature "KCTD17" (0.000007)
2766. feature "DNAAF8" (0.000007)
2767. feature "ZNF354C" (0.000007)
2768. feature "ZNF969P" (0.000007)
2769. feature "CATSPERB" (0.000007)
2770. feature "TMEM80" (0.000007)
2771. feature "SHOX2" (0.000007)
2772. feature "LINC01214" (0.000007)
2773. feature "OR9H1P" (0.000007)
2774. feature "ARRDC3-AS1" (0.000007)
2775. feature "SYCP3" (0.000007)
2776. feature "OXTR" (0.000007)
2777. feature "HMOX1" (0.000007)
2778. feature "RABL2A" (0.000007)
2779. feature "DENND6B" (0.000007)
2780. feature "USP7-AS1" (0.000007)
2781. feature "ENGASE" (0.000007)
2782. feature "MFSD14A" (0.000007)
2783. feature "SDCBP2-AS1" (0.000007)
2784. feature "PDZD2" (0.000007)
2785. feature "B3GNT5" (0.000007)
2786. feature "CRB3" (0.000007)
2787. feature "ZNF512" (0.000007)
2788. feature "ACTR5" (0.000007)
2789. feature "IKZF4" (0.000007)
2790. feature "ARMC2" (0.000007)
2791. feature "MMAA" (0.000007)
2792. feature "LZTR1" (0.000007)
2793. feature "CAPN10-DT" (0.000007)
2794. feature "ETV6" (0.000007)
2795. feature "PHF1" (0.000007)
2796. feature "RADIL" (0.000006)
2797. feature "ZNF25" (0.000006)
2798. feature "FGD3" (0.000006)
2799. feature "PCBP4" (0.000006)
2800. feature "CPEB2" (0.000006)
2801. feature "WDR90" (0.000006)
2802. feature "STAR" (0.000006)
2803. feature "TBC1D27P" (0.000006)
2804. feature "RNF19B" (0.000006)
2805. feature "PRTG" (0.000006)
2806. feature "KDM7A-DT" (0.000006)
2807. feature "ZBTB12" (0.000006)
2808. feature "ERVMER34-1" (0.000006)
2809. feature "CRAT" (0.000006)
2810. feature "KCNF1" (0.000006)
2811. feature "HOTAIR" (0.000006)
2812. feature "NIBAN3" (0.000006)
2813. feature "TAT-AS1" (0.000006)
2814. feature "RAP1GAP" (0.000006)
2815. feature "CDK18" (0.000006)
2816. feature "ITPRIP" (0.000006)
2817. feature "UBQLN1-AS1" (0.000006)
2818. feature "CAPN5" (0.000006)
2819. feature "HDAC4" (0.000006)
2820. feature "TRABD2A" (0.000006)
2821. feature "FAM184A" (0.000006)
2822. feature "COL6A1" (0.000005)
2823. feature "FANCE" (0.000005)
2824. feature "GIPR" (0.000005)
2825. feature "FAM86C2P" (0.000005)
2826. feature "SLC29A2" (0.000005)
2827. feature "TPPP3" (0.000005)
2828. feature "FUT2" (0.000005)
2829. feature "INAFM2" (0.000005)
2830. feature "ATP13A4" (0.000005)
2831. feature "CCDC102A" (0.000005)
2832. feature "ZNF615" (0.000005)
2833. feature "ACTG1P25" (0.000005)
2834. feature "FOXP3" (0.000005)
2835. feature "PLCG2" (0.000005)
2836. feature "GASK1B" (0.000005)
2837. feature "SCAMP5" (0.000005)
2838. feature "ZNF569" (0.000005)
2839. feature "PAQR3" (0.000005)
2840. feature "SHISA4" (0.000005)
2841. feature "LINC00352" (0.000005)
2842. feature "FHIP2B" (0.000005)
2843. feature "MT-TT" (0.000005)
2844. feature "EPHX2" (0.000005)
2845. feature "RPS6P26" (0.000005)
2846. feature "PCDH11X" (0.000005)
2847. feature "TMCO6" (0.000005)
2848. feature "PLEKHS1" (0.000005)
2849. feature "MFAP3" (0.000005)
2850. feature "PSEN2" (0.000005)
2851. feature "SIRT3" (0.000005)
2852. feature "MTCO1P40" (0.000005)
2853. feature "TMCC1-DT" (0.000005)
2854. feature "EPS15-AS1" (0.000005)
2855. feature "GCNT2" (0.000005)
2856. feature "ARHGEF40" (0.000005)
2857. feature "DUSP7" (0.000005)
2858. feature "DPY19L2P1" (0.000005)
2859. feature "RGMB" (0.000005)
2860. feature "FGFR2" (0.000005)
2861. feature "LINC02163" (0.000005)
2862. feature "RPL10AP6" (0.000005)
2863. feature "SLC4A11" (0.000005)
2864. feature "ELAPOR2" (0.000005)
2865. feature "SMIM3" (0.000005)
2866. feature "CHST6" (0.000004)
2867. feature "MAFA" (0.000004)
2868. feature "LINC00680" (0.000004)
2869. feature "SYNM" (0.000004)
2870. feature "ZDHHC8" (0.000004)
2871. feature "STOX1" (0.000004)
2872. feature "LINC00885" (0.000004)
2873. feature "TTPA" (0.000004)
2874. feature "P2RX7" (0.000004)
2875. feature "RNF157" (0.000004)
2876. feature "LIMD2" (0.000004)
2877. feature "WDR19" (0.000004)
2878. feature "LINC01355" (0.000004)
2879. feature "LINC00513" (0.000004)
2880. feature "GHDC" (0.000004)
2881. feature "LINC02080" (0.000004)
2882. feature "GPR68" (0.000004)
2883. feature "LPP-AS2" (0.000004)
2884. feature "ABHD8" (0.000004)
2885. feature "OTUB2" (0.000004)
2886. feature "SCMH1" (0.000004)
2887. feature "LINC01270" (0.000004)
2888. feature "NAP1L6P" (0.000004)
2889. feature "LINC02710" (0.000004)
2890. feature "HROB" (0.000004)
2891. feature "GCM1" (0.000004)
2892. feature "P2RY2" (0.000004)
2893. feature "TIRAP" (0.000004)
2894. feature "ASAH2B" (0.000004)
2895. feature "ZNF90" (0.000004)
2896. feature "SMOX" (0.000004)
2897. feature "NDRG4" (0.000004)
2898. feature "IL18BP" (0.000004)
2899. feature "NKAIN1" (0.000003)
2900. feature "H2AZ2P1" (0.000003)
2901. feature "FAM214B" (0.000003)
2902. feature "NEBL-AS1" (0.000003)
2903. feature "MT-TK" (0.000003)
2904. feature "IPO8P1" (0.000003)
2905. feature "RAB4B" (0.000003)
2906. feature "FER1L5" (0.000003)
2907. feature "EFNB1" (0.000003)
2908. feature "TMEM191C" (0.000003)
2909. feature "KCNS3" (0.000003)
2910. feature "ARHGEF26-AS1" (0.000003)
2911. feature "FRAS1" (0.000003)
2912. feature "PARS2" (0.000003)
2913. feature "HSH2D" (0.000003)
2914. feature "NPHP4" (0.000003)
2915. feature "GUCY1B2" (0.000003)
2916. feature "VEPH1" (0.000003)
2917. feature "F3" (0.000003)
2918. feature "MARCKSL1P2" (0.000003)
2919. feature "TMEM216" (0.000003)
2920. feature "LINC01722" (0.000003)
2921. feature "CAB39L" (0.000003)
2922. feature "AIMP1P1" (0.000003)
2923. feature "NAT8L" (0.000003)
2924. feature "C5orf64" (0.000003)
2925. feature "TMEM169" (0.000003)
2926. feature "ATG4C" (0.000003)
2927. feature "GALNT18" (0.000003)
2928. feature "CA4" (0.000003)
2929. feature "OAF" (0.000002)
2930. feature "SUNO1" (0.000002)
2931. feature "BBS7-DT" (0.000002)
2932. feature "KLHL21" (0.000002)
2933. feature "ZNF286B" (0.000002)
2934. feature "KHDC1" (0.000002)
2935. feature "GUCY1A2" (0.000002)
2936. feature "NARF-AS2" (0.000002)
2937. feature "PSMC1P10" (0.000002)
2938. feature "PGAP3" (0.000002)
2939. feature "PSORS1C1" (0.000002)
2940. feature "RBM12B-AS1" (0.000002)
2941. feature "STAMBPL1" (0.000002)
2942. feature "RUSC2" (0.000002)
2943. feature "DIRC3" (0.000002)
2944. feature "SAMD10" (0.000002)
2945. feature "TRIM31" (0.000002)
2946. feature "MC2R" (0.000002)
2947. feature "AARSD1" (0.000002)
2948. feature "IL17D" (0.000002)
2949. feature "DRC3" (0.000002)
2950. feature "LINC00997" (0.000002)
2951. feature "TSHR" (0.000002)
2952. feature "TNS4" (0.000002)
2953. feature "LENG9" (0.000002)
2954. feature "KCNG1" (0.000002)
2955. feature "LY6E-DT" (0.000002)
2956. feature "WASH6P" (0.000002)
2957. feature "IFFO2" (0.000002)
2958. feature "ZNF865" (0.000002)
2959. feature "DISC1" (0.000002)
2960. feature "NPTXR" (0.000002)
2961. feature "LINC01637" (0.000002)
2962. feature "ZNF436" (0.000002)
2963. feature "BMP8A" (0.000001)
2964. feature "EID2B" (0.000001)
2965. feature "MAS1" (0.000001)
2966. feature "GPR153" (0.000001)
2967. feature "SH3BP5L" (0.000001)
2968. feature "RFLNB" (0.000000)
2969. feature "MUC5AC" (0.000000)
2970. feature "FSTL4" (0.000000)
2971. feature "RUBCN" (0.000000)
2972. feature "WDR81" (0.000000)
2973. feature "SYNPO2L" (0.000000)
2974. feature "DMTN" (0.000000)
2975. feature "GOLGA8B" (0.000000)
2976. feature "TCF4" (0.000000)
2977. feature "PDZRN3" (0.000000)
2978. feature "ZNF674-AS1" (0.000000)
2979. feature "MTRNR2L4" (0.000000)
2980. feature "LINC00271" (0.000000)
2981. feature "STAG3" (0.000000)
2982. feature "UQCRHL" (0.000000)
2983. feature "LRRC8E" (0.000000)
2984. feature "GNAZ" (0.000000)
2985. feature "COLCA1" (0.000000)
2986. feature "ARL6" (0.000000)
2987. feature "TRIM62" (0.000000)
2988. feature "NCF2" (0.000000)
2989. feature "C9orf152" (0.000000)
2990. feature "ZNF30" (0.000000)
2991. feature "SCRN2" (0.000000)
2992. feature "TBCCD1" (0.000000)
2993. feature "SNN" (0.000000)
2994. feature "ACTA2" (0.000000)
2995. feature "PDE10A" (0.000000)
2996. feature "OR4F13P" (0.000000)
2997. feature "PPT2" (0.000000)
2998. feature "ZBTB49" (0.000000)
2999. feature "LINC00863" (0.000000)
3000. feature "ZNF571-AS1" (0.000000)
¶
4.3.3) XGBoost and Adaptive Boosting Classifier
Also in this case we tried to see if the Xgboost predictor could beat the performance of the Random forest classifier.
Indeed it happened, we built the model as the previous one, but augmenting the parameter search with the same numbers of the random forest classifier.
We were satisfied as the performance of this model was slightly better, indeed it achieves a precision of 97,70%.
Also in this case the gene MT_RNR2 was very high in the ranking of the predictors.
#i build a XGBoost classifier for the DropSeq MCF7 dataset to see if it performs better than the random forest classifier
# Define the XGBoost classifier
xgb = XGBClassifier(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [50, 60, 100], 'max_depth': [30, 50, 60]}
grid_search = GridSearchCV(xgb, param_grid, cv=5)
outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)
# Perform the cross validation
scores = cross_val_score(grid_search, X_train_df_DS_MCF7, y_train_df_DS_MCF7, cv=outer_cv)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))
# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_DS_MCF7, y_train_df_DS_MCF7)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_DS_MCF7, y_test_df_DS_MCF7))
# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_DS_MCF7.shape[1]):
print("%d. feature %s (%f)" % (f + 1, X_train_df_DS_MCF7.columns[indices[f]], importances[indices[f]]))
best_xgb_DS_MCF7 = grid_search.best_estimator_
Mean accuracy of nested cross validation: 0.9766473988439307
Best hyperparameters: {'max_depth': 50, 'n_estimators': 100}
Best accuracy: 0.976878612716763
Best model: XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=50, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=100, n_jobs=None,
num_parallel_tree=None, random_state=42, ...)
Test accuracy: 0.9798890429958391
Feature ranking:
1. feature "TFF1" (0.046183)
2. feature "GAPDH" (0.045575)
3. feature "MT-RNR2" (0.033950)
4. feature "KRT19" (0.027494)
5. feature "PGK1" (0.026391)
6. feature "LGALS1" (0.022712)
7. feature "TMSB10" (0.018845)
8. feature "MT-RNR1" (0.017575)
9. feature "RNPS1" (0.008641)
10. feature "MT-ND3" (0.006656)
11. feature "MT-ND4" (0.006061)
12. feature "MT-CO3" (0.005830)
13. feature "ENO1" (0.005807)
14. feature "MT-ND1" (0.005790)
15. feature "MT-CYB" (0.005780)
16. feature "TPI1" (0.005340)
17. feature "MT-ND2" (0.005092)
18. feature "LIMK1" (0.005059)
19. feature "RPS19" (0.004945)
20. feature "NDUFAF8" (0.004617)
21. feature "ZNF326" (0.004367)
22. feature "ZMYND11" (0.004317)
23. feature "RPS14" (0.004219)
24. feature "PPP4R3A" (0.004003)
25. feature "MT-TS1" (0.003910)
26. feature "PKM" (0.003838)
27. feature "HMGB2" (0.003778)
28. feature "DNAJA3" (0.003773)
29. feature "METRN" (0.003753)
30. feature "ROCK1" (0.003692)
31. feature "SYTL2" (0.003690)
32. feature "KCNQ1OT1" (0.003664)
33. feature "RIPK2" (0.003655)
34. feature "MT-ATP6" (0.003639)
35. feature "RPL13" (0.003486)
36. feature "PRR12" (0.003460)
37. feature "LRRCC1" (0.003242)
38. feature "IFI27L2" (0.003217)
39. feature "AAMP" (0.003180)
40. feature "RAD23B" (0.003125)
41. feature "SPACA6" (0.003035)
42. feature "RPS27L" (0.003005)
43. feature "WSB2" (0.002999)
44. feature "SETD2" (0.002954)
45. feature "CSK" (0.002896)
46. feature "PPP2R3B" (0.002865)
47. feature "NRBP1" (0.002862)
48. feature "FARSA" (0.002860)
49. feature "MT-ND6" (0.002853)
50. feature "DDX28" (0.002852)
51. feature "ZNF121" (0.002848)
52. feature "SAPCD2" (0.002847)
53. feature "TIMELESS" (0.002769)
54. feature "SYCP2" (0.002730)
55. feature "MIOS" (0.002707)
56. feature "MT-CO2" (0.002704)
57. feature "PPFIA1" (0.002695)
58. feature "SOGA1" (0.002642)
59. feature "CDC42SE1" (0.002641)
60. feature "CNOT2" (0.002630)
61. feature "EIF3J" (0.002625)
62. feature "HES1" (0.002592)
63. feature "RPLP0P2" (0.002552)
64. feature "PTGES2" (0.002520)
65. feature "EIF2B4" (0.002512)
66. feature "INCENP" (0.002506)
67. feature "TMEM64" (0.002476)
68. feature "FASN" (0.002460)
69. feature "DSP" (0.002449)
70. feature "GFOD1" (0.002441)
71. feature "TOB1" (0.002436)
72. feature "CDK2AP1" (0.002433)
73. feature "MAFF" (0.002432)
74. feature "RAP1A" (0.002404)
75. feature "ZHX1" (0.002342)
76. feature "ABCB9" (0.002337)
77. feature "DTX3" (0.002334)
78. feature "ZFC3H1" (0.002328)
79. feature "KPNA1" (0.002315)
80. feature "SMIM27" (0.002297)
81. feature "STUB1" (0.002288)
82. feature "MT-ND4L" (0.002277)
83. feature "NSRP1" (0.002275)
84. feature "PTBP3" (0.002271)
85. feature "RPL15" (0.002220)
86. feature "ZZZ3" (0.002210)
87. feature "RPL35" (0.002173)
88. feature "SETD3" (0.002152)
89. feature "RNF32" (0.002124)
90. feature "C14orf28" (0.002115)
91. feature "ARHGEF12" (0.002096)
92. feature "SCRIB" (0.002089)
93. feature "OR7E25P" (0.002064)
94. feature "MIR663AHG" (0.002056)
95. feature "LARP4B" (0.002054)
96. feature "MGP" (0.002054)
97. feature "RFNG" (0.002027)
98. feature "MRGBP" (0.002008)
99. feature "MYO10" (0.001994)
100. feature "ATMIN" (0.001974)
101. feature "GSPT1" (0.001960)
102. feature "SOX4" (0.001956)
103. feature "C8orf82" (0.001952)
104. feature "C5" (0.001949)
105. feature "MT-ND5" (0.001931)
106. feature "ARHGAP1" (0.001925)
107. feature "TFF3" (0.001917)
108. feature "DENND3" (0.001906)
109. feature "ATP5MK" (0.001899)
110. feature "UBALD1" (0.001892)
111. feature "TMEM115" (0.001879)
112. feature "NMI" (0.001855)
113. feature "MT-CO1" (0.001845)
114. feature "SLC25A24" (0.001841)
115. feature "RPL34" (0.001839)
116. feature "PLCD3" (0.001834)
117. feature "KIAA1522" (0.001817)
118. feature "BAP1" (0.001803)
119. feature "SOCS2" (0.001793)
120. feature "VPS16" (0.001788)
121. feature "ATRX" (0.001787)
122. feature "GOLGA5" (0.001787)
123. feature "H4C3" (0.001781)
124. feature "MARK2" (0.001774)
125. feature "RPL12" (0.001769)
126. feature "RPL30" (0.001766)
127. feature "RPS5" (0.001760)
128. feature "PLEKHF2" (0.001747)
129. feature "DPH1" (0.001746)
130. feature "MED13" (0.001745)
131. feature "HSPH1" (0.001727)
132. feature "TAF13" (0.001724)
133. feature "AXL" (0.001722)
134. feature "UGCG" (0.001721)
135. feature "NEAT1" (0.001719)
136. feature "MRPL20-AS1" (0.001717)
137. feature "EHBP1" (0.001707)
138. feature "ZFP36L1" (0.001702)
139. feature "RPL39" (0.001701)
140. feature "MT-TQ" (0.001698)
141. feature "S100A10" (0.001689)
142. feature "ZNF26" (0.001686)
143. feature "GABPB2" (0.001683)
144. feature "KLHL7" (0.001680)
145. feature "NIPBL" (0.001679)
146. feature "MYBL1" (0.001656)
147. feature "DYNLL2" (0.001652)
148. feature "KITLG" (0.001621)
149. feature "TPM3" (0.001611)
150. feature "GPM6A" (0.001611)
151. feature "SNRPD2" (0.001603)
152. feature "MBD5" (0.001601)
153. feature "BCYRN1" (0.001599)
154. feature "UBA52" (0.001593)
155. feature "H1-5" (0.001591)
156. feature "CHERP" (0.001585)
157. feature "ZMYM3" (0.001578)
158. feature "UQCRQ" (0.001571)
159. feature "LMX1B" (0.001569)
160. feature "RTF1" (0.001564)
161. feature "FGF23" (0.001557)
162. feature "HAP1" (0.001551)
163. feature "SET" (0.001550)
164. feature "HNRNPUL1" (0.001549)
165. feature "ITPRID2" (0.001539)
166. feature "KRT80" (0.001526)
167. feature "PITPNA" (0.001525)
168. feature "SYNE2" (0.001513)
169. feature "MUC5B" (0.001505)
170. feature "OTULIN" (0.001492)
171. feature "POLR2J" (0.001483)
172. feature "DCLRE1B" (0.001480)
173. feature "B3GALT9" (0.001472)
174. feature "MALAT1" (0.001463)
175. feature "UHRF1BP1" (0.001453)
176. feature "PARD6B" (0.001446)
177. feature "NPLOC4" (0.001445)
178. feature "ZNF316" (0.001439)
179. feature "C6orf62" (0.001425)
180. feature "EHD1" (0.001424)
181. feature "MT-TV" (0.001422)
182. feature "SUDS3" (0.001401)
183. feature "PAPOLA" (0.001393)
184. feature "GATAD2A" (0.001393)
185. feature "PREX1" (0.001393)
186. feature "NDUFB2" (0.001391)
187. feature "DNMT1" (0.001391)
188. feature "RRP1B" (0.001388)
189. feature "H2AC12" (0.001387)
190. feature "TASOR2" (0.001378)
191. feature "PRPF40A" (0.001362)
192. feature "PIGS" (0.001357)
193. feature "ZC3H18" (0.001353)
194. feature "EPB41L1" (0.001345)
195. feature "PLBD2" (0.001330)
196. feature "C7orf50" (0.001327)
197. feature "ROMO1" (0.001327)
198. feature "HIF1A" (0.001319)
199. feature "KMT2B" (0.001316)
200. feature "HDGF" (0.001316)
201. feature "HNRNPA2B1" (0.001315)
202. feature "KRT18" (0.001311)
203. feature "MAPK1IP1L" (0.001310)
204. feature "IMPA2" (0.001304)
205. feature "TPX2" (0.001300)
206. feature "PDAP1" (0.001298)
207. feature "RPS15" (0.001298)
208. feature "H2AC20" (0.001295)
209. feature "FBXO27" (0.001292)
210. feature "MAN1A2" (0.001288)
211. feature "H2AC11" (0.001288)
212. feature "SPAG9" (0.001277)
213. feature "ZBTB20" (0.001274)
214. feature "EBAG9" (0.001267)
215. feature "C9orf78" (0.001264)
216. feature "CEP55" (0.001254)
217. feature "RPL37A" (0.001246)
218. feature "CBX3" (0.001234)
219. feature "NFX1" (0.001232)
220. feature "RPLP2" (0.001225)
221. feature "MCL1" (0.001224)
222. feature "PSMC5" (0.001220)
223. feature "ARF3" (0.001220)
224. feature "UBE2K" (0.001214)
225. feature "TSHZ1" (0.001213)
226. feature "VMP1" (0.001202)
227. feature "TRMT12" (0.001200)
228. feature "LARP1" (0.001194)
229. feature "TNRC6A" (0.001192)
230. feature "RPL29" (0.001189)
231. feature "ANKIB1" (0.001188)
232. feature "IRAK1" (0.001181)
233. feature "RPS16" (0.001176)
234. feature "UGDH" (0.001172)
235. feature "H1-4" (0.001171)
236. feature "MYH9" (0.001169)
237. feature "ZNF444" (0.001168)
238. feature "SPIN1" (0.001164)
239. feature "MAVS" (0.001164)
240. feature "PACS1" (0.001161)
241. feature "PSME4" (0.001156)
242. feature "MT-ATP8" (0.001150)
243. feature "RPL36" (0.001147)
244. feature "DDX21" (0.001147)
245. feature "RPL23" (0.001146)
246. feature "POLDIP2" (0.001145)
247. feature "SLC9A3R1" (0.001140)
248. feature "UQCC2" (0.001137)
249. feature "SRGAP1" (0.001137)
250. feature "POLD2" (0.001136)
251. feature "FAM13B" (0.001131)
252. feature "JAK1" (0.001127)
253. feature "RILPL2" (0.001124)
254. feature "TRIM37" (0.001122)
255. feature "CACNG4" (0.001119)
256. feature "BOD1L1" (0.001116)
257. feature "BCAS3" (0.001109)
258. feature "RPL12P38" (0.001109)
259. feature "CHN1" (0.001108)
260. feature "PHF8" (0.001107)
261. feature "UBE2Q2" (0.001104)
262. feature "KCTD15" (0.001102)
263. feature "TPM4" (0.001102)
264. feature "DROSHA" (0.001099)
265. feature "CAV1" (0.001096)
266. feature "REST" (0.001092)
267. feature "ARPP19" (0.001085)
268. feature "SSRP1" (0.001080)
269. feature "LEO1" (0.001073)
270. feature "DDX54" (0.001072)
271. feature "CACNA1A" (0.001068)
272. feature "STRBP" (0.001066)
273. feature "FAM177A1" (0.001063)
274. feature "COX7A2" (0.001059)
275. feature "HNRNPD" (0.001058)
276. feature "KIF21A" (0.001057)
277. feature "TMEM258" (0.001055)
278. feature "AP1G1" (0.001054)
279. feature "BRIP1" (0.001052)
280. feature "MAPKAPK2" (0.001046)
281. feature "RSRC2" (0.001044)
282. feature "C8orf33" (0.001031)
283. feature "NAA50" (0.001029)
284. feature "CETN3" (0.001023)
285. feature "RPS27" (0.001021)
286. feature "SLITRK5" (0.001020)
287. feature "ALKBH5" (0.001010)
288. feature "RPS15A" (0.001006)
289. feature "SERF2" (0.001001)
290. feature "GOLGA3" (0.001000)
291. feature "CDC42BPB" (0.000993)
292. feature "LINC01304" (0.000992)
293. feature "GPATCH4" (0.000991)
294. feature "STARD10" (0.000990)
295. feature "RRP15" (0.000988)
296. feature "SRSF1" (0.000982)
297. feature "EEA1" (0.000981)
298. feature "NDUFS6" (0.000971)
299. feature "RPL28" (0.000970)
300. feature "TRAK1" (0.000969)
301. feature "SECISBP2L" (0.000967)
302. feature "PRKACA" (0.000955)
303. feature "AP2S1" (0.000954)
304. feature "USP38" (0.000952)
305. feature "PAK2" (0.000950)
306. feature "GSE1" (0.000947)
307. feature "ANKRD11" (0.000943)
308. feature "UQCC3" (0.000942)
309. feature "PRRC2C" (0.000941)
310. feature "ROCK2" (0.000939)
311. feature "LMNB2" (0.000930)
312. feature "ARFGEF1" (0.000929)
313. feature "ZNRF1" (0.000929)
314. feature "H1-0" (0.000923)
315. feature "CENPF" (0.000922)
316. feature "DTYMK" (0.000916)
317. feature "LYAR" (0.000915)
318. feature "ELOB" (0.000914)
319. feature "RPS6KB1" (0.000911)
320. feature "EIF3A" (0.000910)
321. feature "PFDN4" (0.000909)
322. feature "ZNF282" (0.000908)
323. feature "PRRG3" (0.000902)
324. feature "S100A11" (0.000901)
325. feature "PTP4A2" (0.000899)
326. feature "TTLL12" (0.000891)
327. feature "KCNJ2" (0.000889)
328. feature "PKIB" (0.000889)
329. feature "REV1" (0.000887)
330. feature "SPATS2L" (0.000887)
331. feature "NET1" (0.000883)
332. feature "MAGEF1" (0.000882)
333. feature "IGFBP5" (0.000877)
334. feature "RPLP1" (0.000875)
335. feature "PSMG3-AS1" (0.000871)
336. feature "YWHAZ" (0.000868)
337. feature "RPL11" (0.000866)
338. feature "RPS12" (0.000864)
339. feature "TPD52L1" (0.000861)
340. feature "SRM" (0.000859)
341. feature "HNRNPH3" (0.000859)
342. feature "RPL37" (0.000858)
343. feature "RPL35A" (0.000850)
344. feature "USP8" (0.000846)
345. feature "ARID4B" (0.000842)
346. feature "SMIM10L1" (0.000832)
347. feature "CAPZA1" (0.000827)
348. feature "PABPN1" (0.000825)
349. feature "BCAR1" (0.000822)
350. feature "MIPOL1" (0.000819)
351. feature "YWHAB" (0.000817)
352. feature "LCA5L" (0.000804)
353. feature "CPEB4" (0.000793)
354. feature "CNBP" (0.000780)
355. feature "BCL3" (0.000779)
356. feature "ACTB" (0.000778)
357. feature "H19" (0.000775)
358. feature "MGRN1" (0.000775)
359. feature "HEPACAM" (0.000773)
360. feature "MKI67" (0.000771)
361. feature "SPDL1" (0.000770)
362. feature "ZNF217" (0.000769)
363. feature "YES1" (0.000765)
364. feature "ATP5ME" (0.000765)
365. feature "NCL" (0.000765)
366. feature "HELB" (0.000764)
367. feature "ARL13B" (0.000763)
368. feature "ANP32E" (0.000761)
369. feature "WDR43" (0.000758)
370. feature "PITX1" (0.000752)
371. feature "CLTB" (0.000751)
372. feature "H1-2" (0.000747)
373. feature "SUPT5H" (0.000738)
374. feature "S100P" (0.000738)
375. feature "CEP78" (0.000737)
376. feature "NPM1P40" (0.000733)
377. feature "PMEPA1" (0.000731)
378. feature "FTL" (0.000730)
379. feature "DNAJA1" (0.000730)
380. feature "CDIPT" (0.000728)
381. feature "RHOBTB3" (0.000727)
382. feature "EFNA5" (0.000726)
383. feature "VPS18" (0.000721)
384. feature "KIF5B" (0.000721)
385. feature "UBE2Q1" (0.000718)
386. feature "RPS28" (0.000717)
387. feature "ZBTB37" (0.000714)
388. feature "PCGF3" (0.000701)
389. feature "BASP1" (0.000699)
390. feature "GOLGA4" (0.000699)
391. feature "EPN3" (0.000693)
392. feature "SPRYD3" (0.000688)
393. feature "FAM102A" (0.000686)
394. feature "RNF122" (0.000681)
395. feature "SUNO1" (0.000677)
396. feature "GYS1" (0.000677)
397. feature "BPTF" (0.000677)
398. feature "CDAN1" (0.000674)
399. feature "NUP62" (0.000673)
400. feature "SMC6" (0.000672)
401. feature "FOXJ3" (0.000669)
402. feature "OAZ3" (0.000667)
403. feature "SLAIN2" (0.000665)
404. feature "EFCAB5" (0.000664)
405. feature "ZDHHC18" (0.000663)
406. feature "EIF4G2" (0.000662)
407. feature "CENPE" (0.000661)
408. feature "DGKZ" (0.000659)
409. feature "DSCAM-AS1" (0.000657)
410. feature "KRT8" (0.000656)
411. feature "EPAS1" (0.000653)
412. feature "MRPL33" (0.000649)
413. feature "CSDE1" (0.000643)
414. feature "FOXK1" (0.000642)
415. feature "SACS" (0.000641)
416. feature "SOCS4" (0.000641)
417. feature "GNAQ" (0.000640)
418. feature "CHAF1A" (0.000639)
419. feature "GATA3" (0.000639)
420. feature "SEPTIN8" (0.000636)
421. feature "PCGF2" (0.000636)
422. feature "BAZ1B" (0.000631)
423. feature "DCTN5" (0.000627)
424. feature "GARS1" (0.000626)
425. feature "GALNT1" (0.000626)
426. feature "H1-3" (0.000624)
427. feature "SMARCD2" (0.000622)
428. feature "HNRNPAB" (0.000620)
429. feature "PEF1" (0.000618)
430. feature "MDM4" (0.000617)
431. feature "SUSD6" (0.000616)
432. feature "WWP1" (0.000611)
433. feature "LINC00205" (0.000609)
434. feature "JMJD8" (0.000607)
435. feature "RAB13" (0.000606)
436. feature "KRT10" (0.000603)
437. feature "NAA16" (0.000595)
438. feature "GRK6" (0.000594)
439. feature "NCBP3" (0.000593)
440. feature "MAP3K9" (0.000591)
441. feature "GNL3L" (0.000591)
442. feature "SEPTIN11" (0.000590)
443. feature "FMNL3" (0.000586)
444. feature "NEDD8" (0.000584)
445. feature "FAM83H" (0.000583)
446. feature "DKC1" (0.000582)
447. feature "ZBTB42" (0.000580)
448. feature "ESRP2" (0.000580)
449. feature "ZNF578" (0.000579)
450. feature "NKIRAS2" (0.000579)
451. feature "ZNF510" (0.000578)
452. feature "USP1" (0.000577)
453. feature "PPM1G" (0.000573)
454. feature "CKS2" (0.000572)
455. feature "TEPSIN" (0.000570)
456. feature "GTF3A" (0.000570)
457. feature "ZC3H15" (0.000568)
458. feature "ZNF232" (0.000566)
459. feature "HSPB1" (0.000563)
460. feature "EIF4G3" (0.000560)
461. feature "NSD1" (0.000554)
462. feature "ITPK1" (0.000544)
463. feature "GBP1P1" (0.000543)
464. feature "NORAD" (0.000541)
465. feature "COX7C" (0.000538)
466. feature "INPP4B" (0.000538)
467. feature "PCSK5" (0.000536)
468. feature "PARM1" (0.000534)
469. feature "FZD1" (0.000534)
470. feature "ANKRD17" (0.000529)
471. feature "PLEC" (0.000526)
472. feature "CASP8AP2" (0.000525)
473. feature "PLA2G15" (0.000520)
474. feature "SAMD12" (0.000517)
475. feature "R3HDM1" (0.000514)
476. feature "FAM214A" (0.000510)
477. feature "SENP6" (0.000507)
478. feature "NCOA3" (0.000500)
479. feature "NCALD" (0.000496)
480. feature "PSMA7" (0.000496)
481. feature "HNRNPU" (0.000495)
482. feature "G6PD" (0.000494)
483. feature "CAMSAP2" (0.000491)
484. feature "MDM2" (0.000491)
485. feature "PABPC1" (0.000491)
486. feature "SRC" (0.000491)
487. feature "KCTD18" (0.000485)
488. feature "TRIP12" (0.000483)
489. feature "DVL3" (0.000481)
490. feature "DFFB" (0.000474)
491. feature "SRP14-DT" (0.000473)
492. feature "RAD21" (0.000470)
493. feature "MAFK" (0.000469)
494. feature "ANP32B" (0.000468)
495. feature "NAA10" (0.000468)
496. feature "CMSS1" (0.000464)
497. feature "SMC4" (0.000463)
498. feature "FGF12" (0.000461)
499. feature "MLX" (0.000460)
500. feature "L3HYPDH" (0.000460)
501. feature "CTHRC1" (0.000459)
502. feature "MT2A" (0.000459)
503. feature "BOLA3" (0.000458)
504. feature "SVIP" (0.000457)
505. feature "MAP3K14-AS1" (0.000457)
506. feature "CACNB2" (0.000454)
507. feature "XBP1" (0.000454)
508. feature "TPM1" (0.000453)
509. feature "OR2AT4" (0.000452)
510. feature "GADD45GIP1" (0.000452)
511. feature "HCFC1" (0.000452)
512. feature "HTATSF1" (0.000452)
513. feature "VAV2" (0.000448)
514. feature "BRD7" (0.000443)
515. feature "SLK" (0.000441)
516. feature "CENPB" (0.000438)
517. feature "EFCAB14" (0.000438)
518. feature "NBN" (0.000437)
519. feature "RSPH1" (0.000436)
520. feature "HMOX1" (0.000434)
521. feature "ZFP82" (0.000433)
522. feature "COTL1" (0.000432)
523. feature "SLC25A25-AS1" (0.000427)
524. feature "ILF3" (0.000424)
525. feature "TARS1" (0.000421)
526. feature "ATAD2" (0.000420)
527. feature "RBBP6" (0.000420)
528. feature "EML6" (0.000417)
529. feature "CALM2" (0.000416)
530. feature "SLC13A5" (0.000414)
531. feature "HSPA4" (0.000414)
532. feature "PRXL2C" (0.000414)
533. feature "TSPYL1" (0.000411)
534. feature "MTMR12" (0.000410)
535. feature "EIF5B" (0.000409)
536. feature "ATP1A1" (0.000408)
537. feature "SMC5" (0.000408)
538. feature "ZNF777" (0.000407)
539. feature "CDC42EP1" (0.000403)
540. feature "ZNF696" (0.000400)
541. feature "NHS" (0.000400)
542. feature "CLIP1" (0.000397)
543. feature "DDX19A" (0.000396)
544. feature "BRD4" (0.000396)
545. feature "PAPOLA-DT" (0.000392)
546. feature "ABL1" (0.000389)
547. feature "PPP2R5D" (0.000386)
548. feature "HSPD1" (0.000386)
549. feature "NEDD4L" (0.000385)
550. feature "ENAH" (0.000385)
551. feature "USP32" (0.000385)
552. feature "ZNF165" (0.000385)
553. feature "PHF3" (0.000379)
554. feature "AFF1" (0.000379)
555. feature "CAPN13" (0.000378)
556. feature "EIF4EBP2" (0.000377)
557. feature "FRS2" (0.000373)
558. feature "TOP2A" (0.000371)
559. feature "RAB17" (0.000368)
560. feature "SLTM" (0.000368)
561. feature "ACKR3" (0.000368)
562. feature "NF2" (0.000366)
563. feature "ZNF85" (0.000365)
564. feature "KPNA2" (0.000365)
565. feature "NCOR2" (0.000364)
566. feature "CDV3" (0.000356)
567. feature "POLK" (0.000354)
568. feature "TRIB3" (0.000353)
569. feature "NPFFR1" (0.000350)
570. feature "S1PR2" (0.000343)
571. feature "RPS12P16" (0.000342)
572. feature "KIF18B" (0.000341)
573. feature "FBP1" (0.000340)
574. feature "PGAM5" (0.000339)
575. feature "LRCH3" (0.000336)
576. feature "FGD5-AS1" (0.000336)
577. feature "HELZ2" (0.000332)
578. feature "GPBP1" (0.000332)
579. feature "PHACTR2" (0.000327)
580. feature "NRIP1" (0.000325)
581. feature "ZNF731P" (0.000324)
582. feature "NSMAF" (0.000321)
583. feature "PPP1R37" (0.000320)
584. feature "CAST" (0.000320)
585. feature "MAP7D1" (0.000319)
586. feature "ECHDC2" (0.000317)
587. feature "ELOA" (0.000312)
588. feature "DHTKD1" (0.000310)
589. feature "DCTN6-DT" (0.000309)
590. feature "SNHG30" (0.000307)
591. feature "CMPK1" (0.000304)
592. feature "PTPN11" (0.000302)
593. feature "CTXN1" (0.000299)
594. feature "MPHOSPH9" (0.000297)
595. feature "OR2I1P" (0.000297)
596. feature "ONECUT2" (0.000296)
597. feature "SENP3" (0.000293)
598. feature "MRPS34" (0.000291)
599. feature "KLHL20" (0.000290)
600. feature "HSPD1P11" (0.000290)
601. feature "BAZ2A" (0.000290)
602. feature "TIAL1" (0.000288)
603. feature "TNFAIP8L3" (0.000287)
604. feature "METAP2" (0.000287)
605. feature "LRRC69" (0.000286)
606. feature "DEK" (0.000285)
607. feature "RNF34" (0.000283)
608. feature "MGLL" (0.000283)
609. feature "WDR82" (0.000283)
610. feature "BAG1" (0.000279)
611. feature "LRIF1" (0.000275)
612. feature "TYW3" (0.000274)
613. feature "CRNDE" (0.000267)
614. feature "AP3D1" (0.000267)
615. feature "ADD2" (0.000265)
616. feature "AREG" (0.000264)
617. feature "PLCB4" (0.000264)
618. feature "CHP1" (0.000260)
619. feature "KIF14" (0.000259)
620. feature "NCKAP1" (0.000258)
621. feature "SPIRE1" (0.000254)
622. feature "CLSPN" (0.000252)
623. feature "RALBP1" (0.000251)
624. feature "ZDHHC6" (0.000250)
625. feature "NECAB1" (0.000248)
626. feature "TBCA" (0.000244)
627. feature "CDC37" (0.000243)
628. feature "NAA15" (0.000240)
629. feature "SHOX" (0.000237)
630. feature "ATP1A1-AS1" (0.000237)
631. feature "FBXO42" (0.000236)
632. feature "NFIC" (0.000234)
633. feature "ALDH2" (0.000232)
634. feature "BMS1" (0.000230)
635. feature "PATJ" (0.000229)
636. feature "NSD2" (0.000223)
637. feature "PKP4" (0.000220)
638. feature "CCDC186" (0.000218)
639. feature "NCOA5" (0.000216)
640. feature "POLR2I" (0.000213)
641. feature "NONO" (0.000213)
642. feature "IMPA1" (0.000211)
643. feature "IWS1" (0.000209)
644. feature "KPNA4" (0.000206)
645. feature "ARHGEF11" (0.000203)
646. feature "COPS9" (0.000192)
647. feature "ERF" (0.000192)
648. feature "ZBTB26" (0.000187)
649. feature "SLC24A2" (0.000170)
650. feature "CPEB2" (0.000167)
651. feature "FAM120A" (0.000167)
652. feature "RBBP8" (0.000166)
653. feature "MARK3" (0.000165)
654. feature "TAF6" (0.000165)
655. feature "ARHGAP21" (0.000162)
656. feature "TIMM8B" (0.000159)
657. feature "ZNF347" (0.000158)
658. feature "TTC39A" (0.000148)
659. feature "RARG" (0.000147)
660. feature "SNX27" (0.000147)
661. feature "BDP1" (0.000143)
662. feature "TJAP1" (0.000141)
663. feature "MAPK9" (0.000140)
664. feature "DEDD" (0.000139)
665. feature "CNNM2" (0.000135)
666. feature "PC" (0.000134)
667. feature "CDKN3" (0.000133)
668. feature "PTPN23" (0.000133)
669. feature "RPS6KA4" (0.000129)
670. feature "PATL1" (0.000128)
671. feature "TNFSF13B" (0.000125)
672. feature "LCMT1" (0.000120)
673. feature "TRIM41" (0.000120)
674. feature "TCHP" (0.000119)
675. feature "ATP6V1E1" (0.000117)
676. feature "ZRANB1" (0.000117)
677. feature "FAAP100" (0.000107)
678. feature "BTBD9" (0.000107)
679. feature "TP53BP2" (0.000106)
680. feature "UBP1" (0.000093)
681. feature "DNAJC21" (0.000089)
682. feature "ATP6V1H" (0.000087)
683. feature "CCNI" (0.000084)
684. feature "SNX24" (0.000081)
685. feature "TRIM44" (0.000081)
686. feature "PPP2R5E" (0.000080)
687. feature "MSR1" (0.000077)
688. feature "TNIK" (0.000076)
689. feature "BTBD6" (0.000075)
690. feature "PANK3" (0.000073)
691. feature "RELA" (0.000068)
692. feature "USP24" (0.000065)
693. feature "DNAJC2" (0.000064)
694. feature "LPP" (0.000063)
695. feature "C8orf88" (0.000062)
696. feature "UNC50" (0.000058)
697. feature "LXN" (0.000057)
698. feature "CPSF2" (0.000054)
699. feature "HS6ST1" (0.000050)
700. feature "RRN3P3" (0.000048)
701. feature "SUMO3" (0.000048)
702. feature "OR10K1" (0.000048)
703. feature "METRNL" (0.000048)
704. feature "ATP1A4" (0.000036)
705. feature "UBE2F" (0.000036)
706. feature "CRK" (0.000035)
707. feature "SLC25A43" (0.000035)
708. feature "MAILR" (0.000030)
709. feature "TARDBP" (0.000030)
710. feature "DCAF4" (0.000029)
711. feature "C4orf48" (0.000028)
712. feature "ZCCHC2" (0.000025)
713. feature "ZC3H13" (0.000025)
714. feature "WDR46" (0.000021)
715. feature "PECR" (0.000017)
716. feature "KHSRP" (0.000012)
717. feature "GIT1" (0.000006)
718. feature "HMGA1" (0.000005)
719. feature "MTCH1" (0.000004)
720. feature "SCARA3" (0.000000)
721. feature "TJP3" (0.000000)
722. feature "PCDHGA1" (0.000000)
723. feature "GDPGP1" (0.000000)
724. feature "HMGB1P8" (0.000000)
725. feature "LDLRAP1" (0.000000)
726. feature "RHD" (0.000000)
727. feature "CNR2" (0.000000)
728. feature "SNX8" (0.000000)
729. feature "PHACTR1" (0.000000)
730. feature "CRAMP1" (0.000000)
731. feature "C9orf152" (0.000000)
732. feature "CCDC102A" (0.000000)
733. feature "NOG" (0.000000)
734. feature "WDR5" (0.000000)
735. feature "PIP5K1C" (0.000000)
736. feature "CARM1" (0.000000)
737. feature "GYG2" (0.000000)
738. feature "TRAK2" (0.000000)
739. feature "PML" (0.000000)
740. feature "HDAC7" (0.000000)
741. feature "SLC52A3" (0.000000)
742. feature "MT-TH" (0.000000)
743. feature "C3orf62" (0.000000)
744. feature "ZYX" (0.000000)
745. feature "EDA" (0.000000)
746. feature "LINC02605" (0.000000)
747. feature "TMEM222" (0.000000)
748. feature "BACH2" (0.000000)
749. feature "PIK3CB" (0.000000)
750. feature "LINC00639" (0.000000)
751. feature "NXN" (0.000000)
752. feature "CYP19A1" (0.000000)
753. feature "ZNF672" (0.000000)
754. feature "LINC00853" (0.000000)
755. feature "LINC01128" (0.000000)
756. feature "HES6" (0.000000)
757. feature "MAPK8IP2" (0.000000)
758. feature "L3MBTL2-AS1" (0.000000)
759. feature "B3GNT6" (0.000000)
760. feature "EN2" (0.000000)
761. feature "SMPD2" (0.000000)
762. feature "ALKBH8" (0.000000)
763. feature "OPA1" (0.000000)
764. feature "PABPC1L" (0.000000)
765. feature "SUSD3" (0.000000)
766. feature "OR9H1P" (0.000000)
767. feature "MARS2" (0.000000)
768. feature "SAMD4B" (0.000000)
769. feature "MCM2" (0.000000)
770. feature "HARBI1" (0.000000)
771. feature "CHST6" (0.000000)
772. feature "TRIM69" (0.000000)
773. feature "ZNF396" (0.000000)
774. feature "MOB3A" (0.000000)
775. feature "PRRC2A" (0.000000)
776. feature "EMP3" (0.000000)
777. feature "KIF9-AS1" (0.000000)
778. feature "MTCO1P40" (0.000000)
779. feature "MYO9B" (0.000000)
780. feature "KIFC2" (0.000000)
781. feature "PLEKHH3" (0.000000)
782. feature "STAM-DT" (0.000000)
783. feature "ITM2C" (0.000000)
784. feature "EPM2A" (0.000000)
785. feature "ZFP3" (0.000000)
786. feature "BICRAL" (0.000000)
787. feature "KIF1C" (0.000000)
788. feature "YTHDF3" (0.000000)
789. feature "INAFM2" (0.000000)
790. feature "PCF11" (0.000000)
791. feature "ZNF571-AS1" (0.000000)
792. feature "CMBL" (0.000000)
793. feature "ELAPOR2" (0.000000)
794. feature "TIRAP" (0.000000)
795. feature "HOMER3" (0.000000)
796. feature "TAF3" (0.000000)
797. feature "HIF3A" (0.000000)
798. feature "HR" (0.000000)
799. feature "ACTR5" (0.000000)
800. feature "ATP2A1-AS1" (0.000000)
801. feature "SIMC1" (0.000000)
802. feature "INMT" (0.000000)
803. feature "NAT8L" (0.000000)
804. feature "AQP11" (0.000000)
805. feature "KYAT3" (0.000000)
806. feature "TMEM104" (0.000000)
807. feature "ANKRD36BP2" (0.000000)
808. feature "HPS6" (0.000000)
809. feature "GMFB" (0.000000)
810. feature "ZNF302" (0.000000)
811. feature "UNC5B" (0.000000)
812. feature "SARS2" (0.000000)
813. feature "EFNA2" (0.000000)
814. feature "IKZF4" (0.000000)
815. feature "CRB3" (0.000000)
816. feature "GCNT2" (0.000000)
817. feature "TGFB2" (0.000000)
818. feature "ABR" (0.000000)
819. feature "XKR6" (0.000000)
820. feature "VPS13B-DT" (0.000000)
821. feature "LINC00352" (0.000000)
822. feature "HBEGF" (0.000000)
823. feature "MMAA" (0.000000)
824. feature "PTPN7" (0.000000)
825. feature "VASN" (0.000000)
826. feature "VEPH1" (0.000000)
827. feature "THAP1" (0.000000)
828. feature "CCDC18-AS1" (0.000000)
829. feature "ZNF699" (0.000000)
830. feature "LINC01355" (0.000000)
831. feature "TNFRSF10B" (0.000000)
832. feature "TUBG2" (0.000000)
833. feature "GPN1" (0.000000)
834. feature "CTBP2" (0.000000)
835. feature "PPOX" (0.000000)
836. feature "MTCL1" (0.000000)
837. feature "RESF1" (0.000000)
838. feature "KCNJ3" (0.000000)
839. feature "CEP162" (0.000000)
840. feature "FBXO30" (0.000000)
841. feature "DNM3" (0.000000)
842. feature "MT-TC" (0.000000)
843. feature "SLC19A1" (0.000000)
844. feature "STRIT1" (0.000000)
845. feature "FBXO11" (0.000000)
846. feature "CAPN10-DT" (0.000000)
847. feature "NFATC2" (0.000000)
848. feature "PARD3B" (0.000000)
849. feature "NES" (0.000000)
850. feature "LRP3" (0.000000)
851. feature "TSBP1-AS1" (0.000000)
852. feature "CEP85" (0.000000)
853. feature "KIF26A" (0.000000)
854. feature "AFAP1" (0.000000)
855. feature "PARVB" (0.000000)
856. feature "MT-TM" (0.000000)
857. feature "S100A2" (0.000000)
858. feature "AGAP3" (0.000000)
859. feature "RAB40C" (0.000000)
860. feature "GAB2" (0.000000)
861. feature "YARS1" (0.000000)
862. feature "ITGB6" (0.000000)
863. feature "GHDC" (0.000000)
864. feature "LIFR" (0.000000)
865. feature "MAP3K12" (0.000000)
866. feature "IPPK" (0.000000)
867. feature "CHMP1A" (0.000000)
868. feature "PIKFYVE" (0.000000)
869. feature "ZNF354C" (0.000000)
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2842. feature "ZBTB45" (0.000000)
2843. feature "ZBTB39" (0.000000)
2844. feature "ZMIZ1" (0.000000)
2845. feature "HYAL2" (0.000000)
2846. feature "ZNF674" (0.000000)
2847. feature "BET1L" (0.000000)
2848. feature "FBXO7" (0.000000)
2849. feature "FRMD3" (0.000000)
2850. feature "ELOVL7" (0.000000)
2851. feature "RASSF3" (0.000000)
2852. feature "RAVER1" (0.000000)
2853. feature "DOC2A" (0.000000)
2854. feature "NR2F1-AS1" (0.000000)
2855. feature "OR4F13P" (0.000000)
2856. feature "KIF13B" (0.000000)
2857. feature "SULT1B1" (0.000000)
2858. feature "TSHR" (0.000000)
2859. feature "SLC25A37" (0.000000)
2860. feature "KIAA0895L" (0.000000)
2861. feature "LINC00598" (0.000000)
2862. feature "PRR34-AS1" (0.000000)
2863. feature "BCL2L2" (0.000000)
2864. feature "NADK" (0.000000)
2865. feature "MIR4458HG" (0.000000)
2866. feature "SNORD3B-1" (0.000000)
2867. feature "PARD3" (0.000000)
2868. feature "ANKMY1" (0.000000)
2869. feature "CNPPD1" (0.000000)
2870. feature "OR7E122P" (0.000000)
2871. feature "ARHGEF28" (0.000000)
2872. feature "ZC3H7A" (0.000000)
2873. feature "ELP3" (0.000000)
2874. feature "RASSF8" (0.000000)
2875. feature "DUSP8" (0.000000)
2876. feature "PYY2" (0.000000)
2877. feature "RAD54L2" (0.000000)
2878. feature "ORAI1" (0.000000)
2879. feature "ARHGAP26" (0.000000)
2880. feature "MIR3677HG" (0.000000)
2881. feature "FBXO46" (0.000000)
2882. feature "EREG" (0.000000)
2883. feature "SLC25A19" (0.000000)
2884. feature "FSBP" (0.000000)
2885. feature "IRF2BPL" (0.000000)
2886. feature "KIF13A" (0.000000)
2887. feature "GPAT3" (0.000000)
2888. feature "MIR99AHG" (0.000000)
2889. feature "EID2" (0.000000)
2890. feature "CAPN15" (0.000000)
2891. feature "CARD18" (0.000000)
2892. feature "GPR137B" (0.000000)
2893. feature "FARP2" (0.000000)
2894. feature "OXLD1" (0.000000)
2895. feature "VAT1" (0.000000)
2896. feature "PPARA" (0.000000)
2897. feature "PLPPR2" (0.000000)
2898. feature "C20orf27" (0.000000)
2899. feature "MRPS2" (0.000000)
2900. feature "FBRS" (0.000000)
2901. feature "IRGQ" (0.000000)
2902. feature "DOLPP1" (0.000000)
2903. feature "PEA15" (0.000000)
2904. feature "RAI14" (0.000000)
2905. feature "PUS7" (0.000000)
2906. feature "ZBTB22" (0.000000)
2907. feature "MYO5C" (0.000000)
2908. feature "NUDT17" (0.000000)
2909. feature "METTL21A" (0.000000)
2910. feature "ANKRD13D" (0.000000)
2911. feature "LPP-AS2" (0.000000)
2912. feature "DBF4" (0.000000)
2913. feature "TBKBP1" (0.000000)
2914. feature "METTL22" (0.000000)
2915. feature "AKT1S1" (0.000000)
2916. feature "ZNF704" (0.000000)
2917. feature "STK35" (0.000000)
2918. feature "TRIM68" (0.000000)
2919. feature "PDE12" (0.000000)
2920. feature "CRTC1" (0.000000)
2921. feature "ULK1" (0.000000)
2922. feature "MYBL2" (0.000000)
2923. feature "NAPRT" (0.000000)
2924. feature "MIS12" (0.000000)
2925. feature "GRASLND" (0.000000)
2926. feature "KCNC3" (0.000000)
2927. feature "SPINDOC" (0.000000)
2928. feature "NIBAN2" (0.000000)
2929. feature "EIF2AK2" (0.000000)
2930. feature "PKP1" (0.000000)
2931. feature "ABTB2" (0.000000)
2932. feature "PINLYP" (0.000000)
2933. feature "POTEC" (0.000000)
2934. feature "PMS2CL" (0.000000)
2935. feature "MAP4K4" (0.000000)
2936. feature "ZNF768" (0.000000)
2937. feature "CHMP4B" (0.000000)
2938. feature "H2AX" (0.000000)
2939. feature "FBXL19" (0.000000)
2940. feature "CNIH3" (0.000000)
2941. feature "DCAF10" (0.000000)
2942. feature "HID1" (0.000000)
2943. feature "ELP1" (0.000000)
2944. feature "TDRD5" (0.000000)
2945. feature "ZNF579" (0.000000)
2946. feature "TATDN2" (0.000000)
2947. feature "ACVR1" (0.000000)
2948. feature "LIPT1" (0.000000)
2949. feature "ANKRD42" (0.000000)
2950. feature "BATF" (0.000000)
2951. feature "DPY19L1" (0.000000)
2952. feature "PRKAG1" (0.000000)
2953. feature "SHROOM2" (0.000000)
2954. feature "PCED1A" (0.000000)
2955. feature "LINC02080" (0.000000)
2956. feature "BICD2" (0.000000)
2957. feature "STX3" (0.000000)
2958. feature "LCLAT1" (0.000000)
2959. feature "KCNK6" (0.000000)
2960. feature "ZNF77" (0.000000)
2961. feature "HCG18" (0.000000)
2962. feature "TRIM31" (0.000000)
2963. feature "LSS" (0.000000)
2964. feature "IL1RAP" (0.000000)
2965. feature "PHF1" (0.000000)
2966. feature "NOMO3" (0.000000)
2967. feature "XRRA1" (0.000000)
2968. feature "TMEM164" (0.000000)
2969. feature "WDR5B-DT" (0.000000)
2970. feature "FAM104A" (0.000000)
2971. feature "HAND2-AS1" (0.000000)
2972. feature "NDEL1" (0.000000)
2973. feature "ZDHHC5" (0.000000)
2974. feature "NCAM1" (0.000000)
2975. feature "ATXN7L1" (0.000000)
2976. feature "CNPY4" (0.000000)
2977. feature "LIN54" (0.000000)
2978. feature "MDM1" (0.000000)
2979. feature "PSCA" (0.000000)
2980. feature "VDR" (0.000000)
2981. feature "REV3L" (0.000000)
2982. feature "CS" (0.000000)
2983. feature "TF" (0.000000)
2984. feature "TMEM69" (0.000000)
2985. feature "BCCIP" (0.000000)
2986. feature "CIC" (0.000000)
2987. feature "UBE2E3" (0.000000)
2988. feature "AIMP1P1" (0.000000)
2989. feature "CDK12" (0.000000)
2990. feature "KRT16" (0.000000)
2991. feature "PCNX3" (0.000000)
2992. feature "NEMP1" (0.000000)
2993. feature "XKR4" (0.000000)
2994. feature "STARD7" (0.000000)
2995. feature "TBC1D10A" (0.000000)
2996. feature "CBX6" (0.000000)
2997. feature "SSH3" (0.000000)
2998. feature "ZBED6" (0.000000)
2999. feature "ZZEF1" (0.000000)
3000. feature "PPP1R3D" (0.000000)
We decided moreover to experiment with another classifier.
We decided to try to train an adaptive boosting classifier, that is known for its simplicity and effectiveness.
It trains a sequence of weak learners iteratively. In each iteration, the algorithm assigns weights to the training instances, with higher weights given to instances that were misclassified by the previous weak learners. This focuses the attention of the subsequent weak learners on the difficult-to-classify instances. After each weak learner is trained, AdaBoost assigns a weight to its prediction based on its accuracy. The algorithm then combines the predictions of all weak learners by weighted voting, where the weight of each weak learner's prediction depends on its accuracy.
In the parameters we used the algorithm 'SAMME.R' as it is an extension of SAMME that provides real-valued class probabilities instead of discrete class labels, allowing the model to produce more flexible output.
The problem of this classifier was that it needed a very long time to find the best hyperparameters and to fit the trainig data to finally have literally the same performance as the xgboost classifier (only 0.03% better).
The good news was that also this model confirmed us the importance of the gene MT-RNR2 for the predictions.
So we still decided to keep it in the report but not to use it in the next DropSeq dataset.
#i want to implement an adaptive boosting classifier for the DropSeq MCF7 dataset to see if it performs better than the random forest and XGBoost classifiers
from sklearn.ensemble import AdaBoostClassifier
# Define the AdaBoost classifier
ada = AdaBoostClassifier(algorithm= 'SAMME' , random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [100, 150], 'learning_rate': [0.5, 1]}
grid_search = GridSearchCV(ada, param_grid, cv=5)
outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)
# Perform the cross validation
scores = cross_val_score(grid_search, X_train_df_DS_MCF7, y_train_df_DS_MCF7, cv=outer_cv)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))
# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_DS_MCF7, y_train_df_DS_MCF7)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
best_ada_DS_MCF7 = grid_search.best_estimator_
print("best model: ", best_ada_DS_MCF7)
#i want to test the model on the test dataset
print("Test accuracy: ", best_ada_DS_MCF7.score(X_test_df_DS_MCF7, y_test_df_DS_MCF7))
# Print the feature importances
importances = best_ada_DS_MCF7.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_DS_MCF7.shape[1]):
print("%d. feature %s (%f)" % (f + 1, X_train_df_DS_MCF7.columns[indices[f]], importances[indices[f]]))
Mean accuracy of nested cross validation: 0.972485549132948
Best hyperparameters: {'learning_rate': 1, 'n_estimators': 150}
Best accuracy: 0.9733526011560694
best model: AdaBoostClassifier(algorithm='SAMME', learning_rate=1, n_estimators=150,
random_state=42)
Test accuracy: 0.9759593157651411
Feature ranking:
1. feature "TMSB10" (0.043231)
2. feature "MT-RNR2" (0.033690)
3. feature "TFF1" (0.030733)
4. feature "MT-RNR1" (0.029706)
5. feature "MALAT1" (0.029389)
6. feature "TPI1" (0.028143)
7. feature "RPL35" (0.025724)
8. feature "NEAT1" (0.025381)
9. feature "MIR663AHG" (0.025306)
10. feature "PGK1" (0.024401)
11. feature "KRT19" (0.023226)
12. feature "BCYRN1" (0.022916)
13. feature "MT-ND3" (0.022755)
14. feature "GAPDH" (0.022508)
15. feature "PKM" (0.021776)
16. feature "MT-ND4" (0.017891)
17. feature "RPL13" (0.017106)
18. feature "ENO1" (0.016967)
19. feature "TOB1" (0.016283)
20. feature "MT-CO3" (0.016189)
21. feature "SET" (0.015024)
22. feature "H4C3" (0.015011)
23. feature "RPS19" (0.014878)
24. feature "MT-CYB" (0.014521)
25. feature "RPL23" (0.013795)
26. feature "LGALS1" (0.012891)
27. feature "DSP" (0.012886)
28. feature "MT-ND2" (0.012753)
29. feature "FGF23" (0.012433)
30. feature "RPS14" (0.011659)
31. feature "HES1" (0.011081)
32. feature "PRRC2C" (0.011047)
33. feature "RPL30" (0.010997)
34. feature "H2AC20" (0.010963)
35. feature "RPL12" (0.010512)
36. feature "PKIB" (0.009693)
37. feature "RPL15" (0.009573)
38. feature "ACTB" (0.009405)
39. feature "MT-ND4L" (0.009283)
40. feature "MT-TS1" (0.009224)
41. feature "TRIM37" (0.009201)
42. feature "TMEM64" (0.008933)
43. feature "TPD52L1" (0.008825)
44. feature "MT-ATP6" (0.008609)
45. feature "RPS5" (0.007764)
46. feature "HDGF" (0.007592)
47. feature "TMEM258" (0.007367)
48. feature "VMP1" (0.007002)
49. feature "RPL35A" (0.006927)
50. feature "SLC9A3R1" (0.006816)
51. feature "BCAS3" (0.006675)
52. feature "PARD6B" (0.006473)
53. feature "NPM1P40" (0.006187)
54. feature "TFF3" (0.006116)
55. feature "GPM6A" (0.006091)
56. feature "RPL34" (0.005995)
57. feature "PAK2" (0.005951)
58. feature "H1-4" (0.005880)
59. feature "WSB2" (0.005740)
60. feature "SNRPD2" (0.005454)
61. feature "CAPZA1" (0.005420)
62. feature "YWHAB" (0.005232)
63. feature "DNAJA1" (0.005192)
64. feature "S100A10" (0.004976)
65. feature "ANP32B" (0.004958)
66. feature "MT-ATP8" (0.004678)
67. feature "GOLGA4" (0.004576)
68. feature "MT-CO2" (0.004437)
69. feature "MT-ND5" (0.004358)
70. feature "HNRNPAB" (0.004358)
71. feature "H2AC11" (0.004310)
72. feature "ZBTB20" (0.004196)
73. feature "NSD1" (0.004066)
74. feature "PFDN4" (0.004045)
75. feature "SNX27" (0.003949)
76. feature "NFIC" (0.003925)
77. feature "PSMA7" (0.003919)
78. feature "SOX4" (0.003866)
79. feature "S100P" (0.003862)
80. feature "HSPD1" (0.003852)
81. feature "RPLP2" (0.003754)
82. feature "MT-ND6" (0.003651)
83. feature "H19" (0.003644)
84. feature "BTN3A2" (0.003621)
85. feature "PROSER1" (0.003567)
86. feature "BAP1" (0.003405)
87. feature "GPATCH4" (0.003391)
88. feature "RPS12" (0.003335)
89. feature "DEK" (0.003091)
90. feature "CSK" (0.002933)
91. feature "SPATS2L" (0.002594)
92. feature "C8orf33" (0.002155)
93. feature "IGFBP5" (0.002132)
94. feature "ZNF799" (0.000000)
95. feature "MCM2" (0.000000)
96. feature "SAMD4B" (0.000000)
97. feature "ITGB8" (0.000000)
98. feature "ABHD16A" (0.000000)
99. feature "PCDHB1" (0.000000)
100. feature "CDR2L" (0.000000)
101. feature "MGAT4A" (0.000000)
102. feature "RFWD3" (0.000000)
103. feature "KAZN" (0.000000)
104. feature "IL18BP" (0.000000)
105. feature "BBS7-DT" (0.000000)
106. feature "FBXL16" (0.000000)
107. feature "NGRN" (0.000000)
108. feature "CCDC81" (0.000000)
109. feature "IL4R" (0.000000)
110. feature "TFE3" (0.000000)
111. feature "NOTCH4" (0.000000)
112. feature "ISCU" (0.000000)
113. feature "TCHP" (0.000000)
114. feature "GASK1B" (0.000000)
115. feature "CCNE1" (0.000000)
116. feature "MYG1-AS1" (0.000000)
117. feature "CLSTN3" (0.000000)
118. feature "GPR137C" (0.000000)
119. feature "TNIK" (0.000000)
120. feature "HMGN1P17" (0.000000)
121. feature "MTARC1" (0.000000)
122. feature "MBD6" (0.000000)
123. feature "TENT2" (0.000000)
124. feature "DROSHA" (0.000000)
125. feature "CACNG4" (0.000000)
126. feature "CCDC51" (0.000000)
127. feature "ZNF142" (0.000000)
128. feature "RABEP1" (0.000000)
129. feature "ITGB8-AS1" (0.000000)
130. feature "TEAD2" (0.000000)
131. feature "RRAGC" (0.000000)
132. feature "SHOX" (0.000000)
133. feature "PTPN23" (0.000000)
134. feature "ATP5ME" (0.000000)
135. feature "ALDH4A1" (0.000000)
136. feature "LINC00885" (0.000000)
137. feature "TJAP1" (0.000000)
138. feature "FAM222A" (0.000000)
139. feature "FOXO6" (0.000000)
140. feature "CAMK2D" (0.000000)
141. feature "TMEM139" (0.000000)
142. feature "SNHG12" (0.000000)
143. feature "CHST6" (0.000000)
144. feature "MFAP3" (0.000000)
145. feature "TRIM69" (0.000000)
146. feature "ZNF571-AS1" (0.000000)
147. feature "TRIM62" (0.000000)
148. feature "UGCG" (0.000000)
149. feature "CCDC18" (0.000000)
150. feature "APOOL" (0.000000)
151. feature "RGP1" (0.000000)
152. feature "HARBI1" (0.000000)
153. feature "OR2I1P" (0.000000)
154. feature "ASB6" (0.000000)
155. feature "ARL6" (0.000000)
156. feature "RFK" (0.000000)
157. feature "CXCL12" (0.000000)
158. feature "LRRC59" (0.000000)
159. feature "MAP3K14" (0.000000)
160. feature "PABPC1L" (0.000000)
161. feature "FAM193B" (0.000000)
162. feature "TGM2" (0.000000)
163. feature "TRIM39" (0.000000)
164. feature "BMS1P23" (0.000000)
165. feature "YTHDF1" (0.000000)
166. feature "POLR3E" (0.000000)
167. feature "EPCAM-DT" (0.000000)
168. feature "INO80E" (0.000000)
169. feature "OXSM" (0.000000)
170. feature "RPSAP48" (0.000000)
171. feature "RUBCN" (0.000000)
172. feature "GRK6" (0.000000)
173. feature "POMK" (0.000000)
174. feature "TCF4" (0.000000)
175. feature "PINK1" (0.000000)
176. feature "EFNB1" (0.000000)
177. feature "SPDEF" (0.000000)
178. feature "DTX3L" (0.000000)
179. feature "AFG3L1P" (0.000000)
180. feature "KLF9" (0.000000)
181. feature "ARHGEF11" (0.000000)
182. feature "KLK6" (0.000000)
183. feature "TTC39A" (0.000000)
184. feature "PAX9" (0.000000)
185. feature "NCOA1" (0.000000)
186. feature "GOLGA8B" (0.000000)
187. feature "FA2H" (0.000000)
188. feature "NOTCH2NLC" (0.000000)
189. feature "PCF11" (0.000000)
190. feature "BCAR1" (0.000000)
191. feature "RBMS3" (0.000000)
192. feature "PAN3" (0.000000)
193. feature "THBS3" (0.000000)
194. feature "SUSD3" (0.000000)
195. feature "EFHD1" (0.000000)
196. feature "LY6E-DT" (0.000000)
197. feature "GMEB2" (0.000000)
198. feature "OTUB2" (0.000000)
199. feature "RPIA" (0.000000)
200. feature "AKAP5" (0.000000)
201. feature "FIZ1" (0.000000)
202. feature "RABGGTB" (0.000000)
203. feature "LYPD6" (0.000000)
204. feature "PPP1R8" (0.000000)
205. feature "SPACA6" (0.000000)
206. feature "ASAH2B" (0.000000)
207. feature "TWNK" (0.000000)
208. feature "MAFF" (0.000000)
209. feature "FOXK2" (0.000000)
210. feature "TTPA" (0.000000)
211. feature "UBE2L6" (0.000000)
212. feature "USP46-DT" (0.000000)
213. feature "SRCAP" (0.000000)
214. feature "IGSF6" (0.000000)
215. feature "CLEC2D" (0.000000)
216. feature "NFKBIZ" (0.000000)
217. feature "ZNF254" (0.000000)
218. feature "MT1F" (0.000000)
219. feature "KCTD2" (0.000000)
220. feature "SH3RF1" (0.000000)
221. feature "NEDD4L" (0.000000)
222. feature "PCED1B-AS1" (0.000000)
223. feature "GDAP2" (0.000000)
224. feature "RPS15" (0.000000)
225. feature "GLIS2" (0.000000)
226. feature "EPN3" (0.000000)
227. feature "WWC3" (0.000000)
228. feature "RPL37P6" (0.000000)
229. feature "USP32" (0.000000)
230. feature "USP38" (0.000000)
231. feature "PABIR2" (0.000000)
232. feature "XRCC2" (0.000000)
233. feature "IGF2BP2" (0.000000)
234. feature "OR9H1P" (0.000000)
235. feature "RBM15B" (0.000000)
236. feature "SDCBP2-AS1" (0.000000)
237. feature "CABLES2" (0.000000)
238. feature "CDKN3" (0.000000)
239. feature "CSDE1" (0.000000)
240. feature "PPP1R9B" (0.000000)
241. feature "SLK" (0.000000)
242. feature "FASN" (0.000000)
243. feature "PTHLH" (0.000000)
244. feature "RPL34P1" (0.000000)
245. feature "HMGB2" (0.000000)
246. feature "FLOT2" (0.000000)
247. feature "ZNF777" (0.000000)
248. feature "RPL12P38" (0.000000)
249. feature "TIMM29" (0.000000)
250. feature "TMEM222" (0.000000)
251. feature "RESF1" (0.000000)
252. feature "MTCL1" (0.000000)
253. feature "PPOX" (0.000000)
254. feature "ROCK1" (0.000000)
255. feature "SYTL2" (0.000000)
256. feature "TUBG2" (0.000000)
257. feature "TNFRSF10B" (0.000000)
258. feature "LINC01355" (0.000000)
259. feature "CCDC18-AS1" (0.000000)
260. feature "ERF" (0.000000)
261. feature "DVL3" (0.000000)
262. feature "THAP1" (0.000000)
263. feature "VEPH1" (0.000000)
264. feature "VASN" (0.000000)
265. feature "PTPN7" (0.000000)
266. feature "MMAA" (0.000000)
267. feature "PARD3B" (0.000000)
268. feature "RBBP8" (0.000000)
269. feature "NES" (0.000000)
270. feature "KCNJ3" (0.000000)
271. feature "ROMO1" (0.000000)
272. feature "CEP162" (0.000000)
273. feature "MOB3A" (0.000000)
274. feature "BACH2" (0.000000)
275. feature "WDR5" (0.000000)
276. feature "ZNF316" (0.000000)
277. feature "CNR2" (0.000000)
278. feature "LDLRAP1" (0.000000)
279. feature "PPP2R3B" (0.000000)
280. feature "OPA1" (0.000000)
281. feature "LINC00352" (0.000000)
282. feature "NFATC2" (0.000000)
283. feature "FBXO30" (0.000000)
284. feature "CDC37" (0.000000)
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2783. feature "KRT86" (0.000000)
2784. feature "SH2B1" (0.000000)
2785. feature "PLAC9P1" (0.000000)
2786. feature "ZNF343" (0.000000)
2787. feature "PLCB4" (0.000000)
2788. feature "ZRANB1" (0.000000)
2789. feature "ANKRD54" (0.000000)
2790. feature "NAA15" (0.000000)
2791. feature "RNFT2" (0.000000)
2792. feature "SPAG9" (0.000000)
2793. feature "SLC26A4-AS1" (0.000000)
2794. feature "AKT2" (0.000000)
2795. feature "SPIN1" (0.000000)
2796. feature "MYD88" (0.000000)
2797. feature "WIPI2" (0.000000)
2798. feature "DPP9" (0.000000)
2799. feature "GUCD1" (0.000000)
2800. feature "IGFBP4" (0.000000)
2801. feature "WWP1" (0.000000)
2802. feature "RPS6KA6" (0.000000)
2803. feature "GATA2" (0.000000)
2804. feature "AGAP2-AS1" (0.000000)
2805. feature "ARAF" (0.000000)
2806. feature "LRFN4" (0.000000)
2807. feature "SPRY1" (0.000000)
2808. feature "NOL4L" (0.000000)
2809. feature "ERVK3-1" (0.000000)
2810. feature "LINC01427" (0.000000)
2811. feature "DCAF4" (0.000000)
2812. feature "TPCN1" (0.000000)
2813. feature "ZBTB10" (0.000000)
2814. feature "ZC3H18" (0.000000)
2815. feature "SMG7" (0.000000)
2816. feature "CNOT3" (0.000000)
2817. feature "PABPC4" (0.000000)
2818. feature "SLC66A1" (0.000000)
2819. feature "DDX19A" (0.000000)
2820. feature "USP1" (0.000000)
2821. feature "NFKB1" (0.000000)
2822. feature "HPCAL1" (0.000000)
2823. feature "LEO1" (0.000000)
2824. feature "THEM4" (0.000000)
2825. feature "IKBKG" (0.000000)
2826. feature "LRRCC1" (0.000000)
2827. feature "CEP128" (0.000000)
2828. feature "DIS3L" (0.000000)
2829. feature "BAIAP2-DT" (0.000000)
2830. feature "SOCS2" (0.000000)
2831. feature "B4GALT1-AS1" (0.000000)
2832. feature "RAD21" (0.000000)
2833. feature "LRRC8E" (0.000000)
2834. feature "GOLGA3" (0.000000)
2835. feature "FGF12" (0.000000)
2836. feature "AP2S1" (0.000000)
2837. feature "TEPSIN" (0.000000)
2838. feature "PPP1R12B" (0.000000)
2839. feature "SBF2" (0.000000)
2840. feature "SSH1" (0.000000)
2841. feature "MTMR12" (0.000000)
2842. feature "PM20D2" (0.000000)
2843. feature "SGO1" (0.000000)
2844. feature "TAF6" (0.000000)
2845. feature "WDR19" (0.000000)
2846. feature "CDK6" (0.000000)
2847. feature "PAQR3" (0.000000)
2848. feature "HNRNPH3" (0.000000)
2849. feature "PARP6" (0.000000)
2850. feature "CNTRL" (0.000000)
2851. feature "DNAJC3-DT" (0.000000)
2852. feature "ZBTB39" (0.000000)
2853. feature "PGAM5" (0.000000)
2854. feature "ZMIZ1" (0.000000)
2855. feature "HYAL2" (0.000000)
2856. feature "C7orf50" (0.000000)
2857. feature "ZNF146" (0.000000)
2858. feature "NXF1" (0.000000)
2859. feature "DCTN1" (0.000000)
2860. feature "B9D1" (0.000000)
2861. feature "CACNA1A" (0.000000)
2862. feature "DDX25" (0.000000)
2863. feature "PRMT6" (0.000000)
2864. feature "ZMYM3" (0.000000)
2865. feature "LINC01725" (0.000000)
2866. feature "NUP98" (0.000000)
2867. feature "UQCC2" (0.000000)
2868. feature "ZNF134" (0.000000)
2869. feature "CNOT9" (0.000000)
2870. feature "GLMP" (0.000000)
2871. feature "ARFGAP1" (0.000000)
2872. feature "DEAF1" (0.000000)
2873. feature "NAA10" (0.000000)
2874. feature "PCLO" (0.000000)
2875. feature "MAN1A2" (0.000000)
2876. feature "TMCC1-DT" (0.000000)
2877. feature "SPATA6L" (0.000000)
2878. feature "ITPRID2" (0.000000)
2879. feature "SNHG18" (0.000000)
2880. feature "SCLY" (0.000000)
2881. feature "FAM177A1" (0.000000)
2882. feature "TRAF3IP1" (0.000000)
2883. feature "RCC2" (0.000000)
2884. feature "UBE2J1" (0.000000)
2885. feature "CHAMP1" (0.000000)
2886. feature "LNCARSR" (0.000000)
2887. feature "PAQR7" (0.000000)
2888. feature "HOTAIR" (0.000000)
2889. feature "PLD6" (0.000000)
2890. feature "ZNF217" (0.000000)
2891. feature "TTL" (0.000000)
2892. feature "C6orf132" (0.000000)
2893. feature "LUZP1" (0.000000)
2894. feature "BCL3" (0.000000)
2895. feature "MGAT4B" (0.000000)
2896. feature "CAMK2B" (0.000000)
2897. feature "RPL36AP26" (0.000000)
2898. feature "SMPD4BP" (0.000000)
2899. feature "SRSF1" (0.000000)
2900. feature "CCND3" (0.000000)
2901. feature "KCTD6" (0.000000)
2902. feature "ZDHHC6" (0.000000)
2903. feature "ZNF597" (0.000000)
2904. feature "FAM214B" (0.000000)
2905. feature "KLHL8" (0.000000)
2906. feature "OPTN" (0.000000)
2907. feature "TBP" (0.000000)
2908. feature "URI1" (0.000000)
2909. feature "KRT10" (0.000000)
2910. feature "COL5A1" (0.000000)
2911. feature "GLE1" (0.000000)
2912. feature "NMI" (0.000000)
2913. feature "ZNF354B" (0.000000)
2914. feature "HIRIP3" (0.000000)
2915. feature "MYLK-AS1" (0.000000)
2916. feature "ELAC1" (0.000000)
2917. feature "CALML3-AS1" (0.000000)
2918. feature "RNF122" (0.000000)
2919. feature "DPY19L1P1" (0.000000)
2920. feature "ADD2" (0.000000)
2921. feature "DNAAF11" (0.000000)
2922. feature "KMT5B" (0.000000)
2923. feature "LRFN3" (0.000000)
2924. feature "PCYT1A" (0.000000)
2925. feature "ZNF969P" (0.000000)
2926. feature "PLA2G15" (0.000000)
2927. feature "UBL4A" (0.000000)
2928. feature "FBXW2" (0.000000)
2929. feature "SEC14L2" (0.000000)
2930. feature "CEP78" (0.000000)
2931. feature "HMGB1P51" (0.000000)
2932. feature "NEUROD1" (0.000000)
2933. feature "WWC1" (0.000000)
2934. feature "CPLX1" (0.000000)
2935. feature "HMGN1P14" (0.000000)
2936. feature "NOSTRIN" (0.000000)
2937. feature "LIN52" (0.000000)
2938. feature "LINC02324" (0.000000)
2939. feature "PITPNC1" (0.000000)
2940. feature "VPS52" (0.000000)
2941. feature "EID2B" (0.000000)
2942. feature "BMPR1B" (0.000000)
2943. feature "PAPOLA-DT" (0.000000)
2944. feature "ZFC3H1" (0.000000)
2945. feature "ZNF572" (0.000000)
2946. feature "DDX21" (0.000000)
2947. feature "RPUSD4" (0.000000)
2948. feature "PARP16" (0.000000)
2949. feature "PTGES3L" (0.000000)
2950. feature "CDV3" (0.000000)
2951. feature "CHRNB1" (0.000000)
2952. feature "SEMA4C" (0.000000)
2953. feature "MYO18A" (0.000000)
2954. feature "SPRED3" (0.000000)
2955. feature "ZWILCH" (0.000000)
2956. feature "RAD23B" (0.000000)
2957. feature "ZNF652" (0.000000)
2958. feature "MTMR2" (0.000000)
2959. feature "PPT2" (0.000000)
2960. feature "GDI1" (0.000000)
2961. feature "UNC119" (0.000000)
2962. feature "ARNTL2" (0.000000)
2963. feature "WDR62" (0.000000)
2964. feature "ARHGAP39" (0.000000)
2965. feature "CALML5" (0.000000)
2966. feature "TXNRD2" (0.000000)
2967. feature "FAM118B" (0.000000)
2968. feature "SLC29A2" (0.000000)
2969. feature "TPM3" (0.000000)
2970. feature "CRKL" (0.000000)
2971. feature "C19orf73" (0.000000)
2972. feature "METRNL" (0.000000)
2973. feature "FAM120A" (0.000000)
2974. feature "EIF5B" (0.000000)
2975. feature "HMBS" (0.000000)
2976. feature "SYT12" (0.000000)
2977. feature "PREX1" (0.000000)
2978. feature "C5orf63" (0.000000)
2979. feature "RNF168" (0.000000)
2980. feature "UQCC3" (0.000000)
2981. feature "MAP3K14-AS1" (0.000000)
2982. feature "LYAR" (0.000000)
2983. feature "RAB23" (0.000000)
2984. feature "DNAJC5" (0.000000)
2985. feature "THNSL2" (0.000000)
2986. feature "BAG1" (0.000000)
2987. feature "PKP3" (0.000000)
2988. feature "AVEN" (0.000000)
2989. feature "CA4" (0.000000)
2990. feature "TFEB" (0.000000)
2991. feature "GTF2F1" (0.000000)
2992. feature "ZNF544" (0.000000)
2993. feature "FOXJ3" (0.000000)
2994. feature "EIPR1" (0.000000)
2995. feature "NIBAN3" (0.000000)
2996. feature "OR2AT4" (0.000000)
2997. feature "SH3PXD2A" (0.000000)
2998. feature "EVPL" (0.000000)
2999. feature "TARDBP" (0.000000)
3000. feature "PPP1R3D" (0.000000)
¶
4.3.4) Neural Network
We now define the neural network model to be used with the dataset dropseq MCF7
# This is the model (neural network) applied to the dataset dropseq MCF7
model_DS_M = NeuralNetworkDropSeq()
model_DS_M
NeuralNetworkDropSeq(
(model): Sequential(
(0): Flatten(start_dim=1, end_dim=-1)
(1): Linear(in_features=3000, out_features=512, bias=True)
(2): LeakyReLU(negative_slope=0.01)
(3): Dropout(p=0.4, inplace=False)
(4): Linear(in_features=512, out_features=256, bias=True)
(5): LeakyReLU(negative_slope=0.01)
(6): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): Dropout(p=0.4, inplace=False)
(8): Linear(in_features=256, out_features=128, bias=True)
(9): LeakyReLU(negative_slope=0.01)
(10): Dropout(p=0.3, inplace=False)
(11): Linear(in_features=128, out_features=64, bias=True)
(12): LeakyReLU(negative_slope=0.01)
(13): Linear(in_features=64, out_features=1, bias=True)
)
)
We split the train dataset and the labels into train dataset and validation dataset (this second one used to validate and see the accuracy during the training phase). Then we convert these variables into tensors in order to be able to use them with pytorch.
# convert split dataset in tensors
X_train_DS_MCF7_nn, X_val_DS_MCF7_nn, y_train_DS_MCF7_nn, y_val_DS_MCF7_nn = train_test_split(X_train_df_DS_MCF7.values, y_train_df_DS_MCF7.values, test_size=0.2, random_state=42)
X_train_tensor_M = torch.tensor(X_train_DS_MCF7_nn, dtype=torch.float32)
y_train_tensor_M = torch.tensor(y_train_DS_MCF7_nn, dtype=torch.float32).unsqueeze(1) # For binary classification
X_val_tensor_M = torch.tensor(X_val_DS_MCF7_nn, dtype=torch.float32)
y_val_tensor_M = torch.tensor(y_val_DS_MCF7_nn, dtype=torch.float32).unsqueeze(1) # For binary classification
¶
Train and validate the neural network drop-seq MCF7
Here below there is the training process of the neural network using the dataset MCF7 dropseq.
The process involves using Stochastic Gradient Descent (SGD) as the optimizer with a learning rate of 0.01, and the loss function is defined as binary cross-entropy with logits ('BCEWithLogitsLoss') which combines a sigmoid activation and binary cross-entropy loss. The training runs for 1000 epochs.
The training loop starts by setting the model to training mode, resetting the gradients, performing a forward pass on the training data, calculating the training loss, and then using backpropagation to compute the gradients. The optimizer updates the model parameters based on these gradients. Finally, training accuracy is calculated by rounding the sigmoid-activated predictions and comparing them to the true labels.
For validation, the model is set to evaluation mode, and a forward pass is performed on the validation data. The validation loss and accuracy are calculated similarly to the training process but without computing gradients.
# Train and validate the neural network drop-seq MCF7
optimizer = torch.optim.SGD(model_DS_M.parameters(), lr=0.01)
criterion = nn.BCEWithLogitsLoss() # applies a sigmoid activation to the model's raw logits and calculates the binary cross-entropy loss
epochs = 1000
best_val_acc = 0
patience = 3
no_improve_epochs = 0
best_model_path_M = 'best_model_final_M.pth'
for epoch in range(epochs):
model_DS_M.train()
optimizer.zero_grad() # resets the gradients of all model parameters to zero.
train_output = model_DS_M(X_train_tensor_M) # Forward pass with all training data, computes the model's predictions (output) for the entire training dataset
train_loss = criterion(train_output, y_train_tensor_M) # calculates the difference between predicted outputs and actual labels
train_loss.backward() #computes the gradients of the loss with respect to the model's parameters
# This uses backpropagation to distribute the error back through the network
optimizer.step() #The optimizer applies the selected algorithm (like SGD) to update the weights
# Calculate training accuracy
with torch.no_grad():
train_predictions = torch.round(torch.sigmoid(train_output))
train_correct = (train_predictions == y_train_tensor_M).sum().item()
train_total = y_train_tensor_M.size(0)
train_acc = train_correct / train_total
# Validation
model_DS_M.eval() # sets the model to evaluation mode, disabling features like dropout and batch normalization to ensure consistency
with torch.no_grad(): #prevents PyTorch from computing gradients during evaluation.
val_output = model_DS_M(X_val_tensor_M) #computes predictions on the validation dataset
val_loss = criterion(val_output, y_val_tensor_M)
val_predictions = torch.round(torch.sigmoid(val_output)) # converts the logits/predictions into binary predictions (0 or 1)
val_correct = (val_predictions == y_val_tensor_M).sum().item()
val_total = y_val_tensor_M.size(0)
val_acc = val_correct / val_total
print(f'Epoch {epoch+1}/{epochs}, '
f'Train Loss: {train_loss.item():.4f}, '
f'Train Accuracy: {train_acc:.4f}, '
f'Val Loss: {val_loss.item():.4f}, '
f'Val Accuracy: {val_acc:.4f}')
# Save model after all epochs are completed
#torch.save(model.state_dict(), best_model_path)
Epoch 1/1000, Train Loss: 0.7007, Train Accuracy: 0.4471, Val Loss: 0.7040, Val Accuracy: 0.4156 Epoch 2/1000, Train Loss: 0.6994, Train Accuracy: 0.4538, Val Loss: 0.7037, Val Accuracy: 0.4156 Epoch 3/1000, Train Loss: 0.6975, Train Accuracy: 0.4587, Val Loss: 0.7034, Val Accuracy: 0.4156 Epoch 4/1000, Train Loss: 0.6972, Train Accuracy: 0.4642, Val Loss: 0.7032, Val Accuracy: 0.4156 Epoch 5/1000, Train Loss: 0.6952, Train Accuracy: 0.4736, Val Loss: 0.7028, Val Accuracy: 0.4156 Epoch 6/1000, Train Loss: 0.6943, Train Accuracy: 0.4798, Val Loss: 0.7025, Val Accuracy: 0.4156 Epoch 7/1000, Train Loss: 0.6922, Train Accuracy: 0.4956, Val Loss: 0.7022, Val Accuracy: 0.4156 Epoch 8/1000, Train Loss: 0.6915, Train Accuracy: 0.5019, Val Loss: 0.7018, Val Accuracy: 0.4156 Epoch 9/1000, Train Loss: 0.6898, Train Accuracy: 0.5069, Val Loss: 0.7015, Val Accuracy: 0.4156 Epoch 10/1000, Train Loss: 0.6884, Train Accuracy: 0.5208, Val Loss: 0.7011, Val Accuracy: 0.4156 Epoch 11/1000, Train Loss: 0.6859, Train Accuracy: 0.5329, Val Loss: 0.7007, Val Accuracy: 0.4156 Epoch 12/1000, Train Loss: 0.6860, Train Accuracy: 0.5395, Val Loss: 0.7003, Val Accuracy: 0.4156 Epoch 13/1000, Train Loss: 0.6854, Train Accuracy: 0.5392, Val Loss: 0.6999, Val Accuracy: 0.4156 Epoch 14/1000, Train Loss: 0.6836, Train Accuracy: 0.5540, Val Loss: 0.6994, Val Accuracy: 0.4156 Epoch 15/1000, Train Loss: 0.6814, Train Accuracy: 0.5653, Val Loss: 0.6989, Val Accuracy: 0.4156 Epoch 16/1000, Train Loss: 0.6804, Train Accuracy: 0.5756, Val Loss: 0.6984, Val Accuracy: 0.4156 Epoch 17/1000, Train Loss: 0.6787, Train Accuracy: 0.5868, Val Loss: 0.6979, Val Accuracy: 0.4156 Epoch 18/1000, Train Loss: 0.6769, Train Accuracy: 0.6012, Val Loss: 0.6973, Val Accuracy: 0.4156 Epoch 19/1000, Train Loss: 0.6759, Train Accuracy: 0.6089, Val Loss: 0.6968, Val Accuracy: 0.4156 Epoch 20/1000, Train Loss: 0.6745, Train Accuracy: 0.6135, Val Loss: 0.6962, Val Accuracy: 0.4156 Epoch 21/1000, Train Loss: 0.6739, Train Accuracy: 0.6259, Val Loss: 0.6955, Val Accuracy: 0.4156 Epoch 22/1000, Train Loss: 0.6732, Train Accuracy: 0.6305, Val Loss: 0.6948, Val Accuracy: 0.4156 Epoch 23/1000, Train Loss: 0.6715, Train Accuracy: 0.6358, Val Loss: 0.6941, Val Accuracy: 0.4156 Epoch 24/1000, Train Loss: 0.6699, Train Accuracy: 0.6485, Val Loss: 0.6934, Val Accuracy: 0.4156 Epoch 25/1000, Train Loss: 0.6670, Train Accuracy: 0.6625, Val Loss: 0.6926, Val Accuracy: 0.4156 Epoch 26/1000, Train Loss: 0.6665, Train Accuracy: 0.6749, Val Loss: 0.6918, Val Accuracy: 0.4156 Epoch 27/1000, Train Loss: 0.6665, Train Accuracy: 0.6728, Val Loss: 0.6909, Val Accuracy: 0.4159 Epoch 28/1000, Train Loss: 0.6638, Train Accuracy: 0.6887, Val Loss: 0.6900, Val Accuracy: 0.4159 Epoch 29/1000, Train Loss: 0.6629, Train Accuracy: 0.6917, Val Loss: 0.6891, Val Accuracy: 0.4162 Epoch 30/1000, Train Loss: 0.6616, Train Accuracy: 0.6955, Val Loss: 0.6881, Val Accuracy: 0.4168 Epoch 31/1000, Train Loss: 0.6596, Train Accuracy: 0.7027, Val Loss: 0.6871, Val Accuracy: 0.4197 Epoch 32/1000, Train Loss: 0.6593, Train Accuracy: 0.7086, Val Loss: 0.6860, Val Accuracy: 0.4275 Epoch 33/1000, Train Loss: 0.6583, Train Accuracy: 0.7169, Val Loss: 0.6848, Val Accuracy: 0.4425 Epoch 34/1000, Train Loss: 0.6555, Train Accuracy: 0.7290, Val Loss: 0.6836, Val Accuracy: 0.4645 Epoch 35/1000, Train Loss: 0.6548, Train Accuracy: 0.7374, Val Loss: 0.6823, Val Accuracy: 0.4913 Epoch 36/1000, Train Loss: 0.6539, Train Accuracy: 0.7360, Val Loss: 0.6810, Val Accuracy: 0.5240 Epoch 37/1000, Train Loss: 0.6516, Train Accuracy: 0.7486, Val Loss: 0.6796, Val Accuracy: 0.5509 Epoch 38/1000, Train Loss: 0.6493, Train Accuracy: 0.7542, Val Loss: 0.6782, Val Accuracy: 0.5853 Epoch 39/1000, Train Loss: 0.6483, Train Accuracy: 0.7560, Val Loss: 0.6767, Val Accuracy: 0.6185 Epoch 40/1000, Train Loss: 0.6469, Train Accuracy: 0.7647, Val Loss: 0.6751, Val Accuracy: 0.6506 Epoch 41/1000, Train Loss: 0.6458, Train Accuracy: 0.7691, Val Loss: 0.6734, Val Accuracy: 0.6780 Epoch 42/1000, Train Loss: 0.6443, Train Accuracy: 0.7709, Val Loss: 0.6717, Val Accuracy: 0.7006 Epoch 43/1000, Train Loss: 0.6430, Train Accuracy: 0.7762, Val Loss: 0.6699, Val Accuracy: 0.7225 Epoch 44/1000, Train Loss: 0.6421, Train Accuracy: 0.7723, Val Loss: 0.6681, Val Accuracy: 0.7434 Epoch 45/1000, Train Loss: 0.6397, Train Accuracy: 0.7835, Val Loss: 0.6661, Val Accuracy: 0.7601 Epoch 46/1000, Train Loss: 0.6387, Train Accuracy: 0.7876, Val Loss: 0.6641, Val Accuracy: 0.7749 Epoch 47/1000, Train Loss: 0.6377, Train Accuracy: 0.7862, Val Loss: 0.6621, Val Accuracy: 0.7864 Epoch 48/1000, Train Loss: 0.6357, Train Accuracy: 0.7979, Val Loss: 0.6600, Val Accuracy: 0.7980 Epoch 49/1000, Train Loss: 0.6338, Train Accuracy: 0.7978, Val Loss: 0.6579, Val Accuracy: 0.8098 Epoch 50/1000, Train Loss: 0.6328, Train Accuracy: 0.8013, Val Loss: 0.6557, Val Accuracy: 0.8182 Epoch 51/1000, Train Loss: 0.6304, Train Accuracy: 0.8078, Val Loss: 0.6534, Val Accuracy: 0.8225 Epoch 52/1000, Train Loss: 0.6292, Train Accuracy: 0.8045, Val Loss: 0.6511, Val Accuracy: 0.8269 Epoch 53/1000, Train Loss: 0.6277, Train Accuracy: 0.8116, Val Loss: 0.6488, Val Accuracy: 0.8353 Epoch 54/1000, Train Loss: 0.6263, Train Accuracy: 0.8132, Val Loss: 0.6465, Val Accuracy: 0.8390 Epoch 55/1000, Train Loss: 0.6247, Train Accuracy: 0.8127, Val Loss: 0.6441, Val Accuracy: 0.8434 Epoch 56/1000, Train Loss: 0.6239, Train Accuracy: 0.8173, Val Loss: 0.6417, Val Accuracy: 0.8491 Epoch 57/1000, Train Loss: 0.6210, Train Accuracy: 0.8189, Val Loss: 0.6393, Val Accuracy: 0.8549 Epoch 58/1000, Train Loss: 0.6195, Train Accuracy: 0.8258, Val Loss: 0.6369, Val Accuracy: 0.8572 Epoch 59/1000, Train Loss: 0.6177, Train Accuracy: 0.8273, Val Loss: 0.6345, Val Accuracy: 0.8621 Epoch 60/1000, Train Loss: 0.6175, Train Accuracy: 0.8250, Val Loss: 0.6321, Val Accuracy: 0.8639 Epoch 61/1000, Train Loss: 0.6145, Train Accuracy: 0.8294, Val Loss: 0.6297, Val Accuracy: 0.8647 Epoch 62/1000, Train Loss: 0.6129, Train Accuracy: 0.8278, Val Loss: 0.6272, Val Accuracy: 0.8688 Epoch 63/1000, Train Loss: 0.6115, Train Accuracy: 0.8329, Val Loss: 0.6248, Val Accuracy: 0.8697 Epoch 64/1000, Train Loss: 0.6095, Train Accuracy: 0.8335, Val Loss: 0.6224, Val Accuracy: 0.8723 Epoch 65/1000, Train Loss: 0.6069, Train Accuracy: 0.8381, Val Loss: 0.6200, Val Accuracy: 0.8723 Epoch 66/1000, Train Loss: 0.6066, Train Accuracy: 0.8348, Val Loss: 0.6176, Val Accuracy: 0.8734 Epoch 67/1000, Train Loss: 0.6044, Train Accuracy: 0.8421, Val Loss: 0.6152, Val Accuracy: 0.8746 Epoch 68/1000, Train Loss: 0.6023, Train Accuracy: 0.8436, Val Loss: 0.6128, Val Accuracy: 0.8777 Epoch 69/1000, Train Loss: 0.6010, Train Accuracy: 0.8401, Val Loss: 0.6105, Val Accuracy: 0.8789 Epoch 70/1000, Train Loss: 0.5990, Train Accuracy: 0.8450, Val Loss: 0.6081, Val Accuracy: 0.8801 Epoch 71/1000, Train Loss: 0.5964, Train Accuracy: 0.8473, Val Loss: 0.6057, Val Accuracy: 0.8821 Epoch 72/1000, Train Loss: 0.5954, Train Accuracy: 0.8459, Val Loss: 0.6033, Val Accuracy: 0.8835 Epoch 73/1000, Train Loss: 0.5928, Train Accuracy: 0.8512, Val Loss: 0.6009, Val Accuracy: 0.8841 Epoch 74/1000, Train Loss: 0.5918, Train Accuracy: 0.8491, Val Loss: 0.5986, Val Accuracy: 0.8844 Epoch 75/1000, Train Loss: 0.5891, Train Accuracy: 0.8482, Val Loss: 0.5961, Val Accuracy: 0.8853 Epoch 76/1000, Train Loss: 0.5867, Train Accuracy: 0.8540, Val Loss: 0.5937, Val Accuracy: 0.8864 Epoch 77/1000, Train Loss: 0.5851, Train Accuracy: 0.8534, Val Loss: 0.5913, Val Accuracy: 0.8879 Epoch 78/1000, Train Loss: 0.5833, Train Accuracy: 0.8538, Val Loss: 0.5889, Val Accuracy: 0.8890 Epoch 79/1000, Train Loss: 0.5796, Train Accuracy: 0.8590, Val Loss: 0.5866, Val Accuracy: 0.8902 Epoch 80/1000, Train Loss: 0.5778, Train Accuracy: 0.8564, Val Loss: 0.5842, Val Accuracy: 0.8910 Epoch 81/1000, Train Loss: 0.5766, Train Accuracy: 0.8589, Val Loss: 0.5818, Val Accuracy: 0.8913 Epoch 82/1000, Train Loss: 0.5733, Train Accuracy: 0.8576, Val Loss: 0.5793, Val Accuracy: 0.8916 Epoch 83/1000, Train Loss: 0.5714, Train Accuracy: 0.8605, Val Loss: 0.5768, Val Accuracy: 0.8916 Epoch 84/1000, Train Loss: 0.5691, Train Accuracy: 0.8639, Val Loss: 0.5744, Val Accuracy: 0.8922 Epoch 85/1000, Train Loss: 0.5667, Train Accuracy: 0.8639, Val Loss: 0.5719, Val Accuracy: 0.8931 Epoch 86/1000, Train Loss: 0.5643, Train Accuracy: 0.8627, Val Loss: 0.5694, Val Accuracy: 0.8951 Epoch 87/1000, Train Loss: 0.5636, Train Accuracy: 0.8634, Val Loss: 0.5669, Val Accuracy: 0.8954 Epoch 88/1000, Train Loss: 0.5591, Train Accuracy: 0.8678, Val Loss: 0.5644, Val Accuracy: 0.8957 Epoch 89/1000, Train Loss: 0.5579, Train Accuracy: 0.8664, Val Loss: 0.5619, Val Accuracy: 0.8960 Epoch 90/1000, Train Loss: 0.5557, Train Accuracy: 0.8634, Val Loss: 0.5593, Val Accuracy: 0.8971 Epoch 91/1000, Train Loss: 0.5532, Train Accuracy: 0.8702, Val Loss: 0.5567, Val Accuracy: 0.8974 Epoch 92/1000, Train Loss: 0.5505, Train Accuracy: 0.8678, Val Loss: 0.5542, Val Accuracy: 0.8977 Epoch 93/1000, Train Loss: 0.5492, Train Accuracy: 0.8721, Val Loss: 0.5516, Val Accuracy: 0.8980 Epoch 94/1000, Train Loss: 0.5467, Train Accuracy: 0.8722, Val Loss: 0.5490, Val Accuracy: 0.8991 Epoch 95/1000, Train Loss: 0.5428, Train Accuracy: 0.8725, Val Loss: 0.5463, Val Accuracy: 0.8991 Epoch 96/1000, Train Loss: 0.5403, Train Accuracy: 0.8743, Val Loss: 0.5437, Val Accuracy: 0.8994 Epoch 97/1000, Train Loss: 0.5395, Train Accuracy: 0.8743, Val Loss: 0.5411, Val Accuracy: 0.9000 Epoch 98/1000, Train Loss: 0.5350, Train Accuracy: 0.8749, Val Loss: 0.5384, Val Accuracy: 0.9000 Epoch 99/1000, Train Loss: 0.5344, Train Accuracy: 0.8712, Val Loss: 0.5357, Val Accuracy: 0.9003 Epoch 100/1000, Train Loss: 0.5312, Train Accuracy: 0.8745, Val Loss: 0.5329, Val Accuracy: 0.9009 Epoch 101/1000, Train Loss: 0.5289, Train Accuracy: 0.8759, Val Loss: 0.5302, Val Accuracy: 0.9009 Epoch 102/1000, Train Loss: 0.5249, Train Accuracy: 0.8795, Val Loss: 0.5274, Val Accuracy: 0.9014 Epoch 103/1000, Train Loss: 0.5219, Train Accuracy: 0.8806, Val Loss: 0.5245, Val Accuracy: 0.9017 Epoch 104/1000, Train Loss: 0.5202, Train Accuracy: 0.8787, Val Loss: 0.5217, Val Accuracy: 0.9023 Epoch 105/1000, Train Loss: 0.5171, Train Accuracy: 0.8798, Val Loss: 0.5188, Val Accuracy: 0.9032 Epoch 106/1000, Train Loss: 0.5157, Train Accuracy: 0.8788, Val Loss: 0.5159, Val Accuracy: 0.9038 Epoch 107/1000, Train Loss: 0.5118, Train Accuracy: 0.8781, Val Loss: 0.5130, Val Accuracy: 0.9038 Epoch 108/1000, Train Loss: 0.5090, Train Accuracy: 0.8805, Val Loss: 0.5101, Val Accuracy: 0.9035 Epoch 109/1000, Train Loss: 0.5050, Train Accuracy: 0.8848, Val Loss: 0.5071, Val Accuracy: 0.9055 Epoch 110/1000, Train Loss: 0.5030, Train Accuracy: 0.8843, Val Loss: 0.5041, Val Accuracy: 0.9058 Epoch 111/1000, Train Loss: 0.4997, Train Accuracy: 0.8854, Val Loss: 0.5011, Val Accuracy: 0.9064 Epoch 112/1000, Train Loss: 0.4978, Train Accuracy: 0.8864, Val Loss: 0.4980, Val Accuracy: 0.9072 Epoch 113/1000, Train Loss: 0.4949, Train Accuracy: 0.8866, Val Loss: 0.4950, Val Accuracy: 0.9072 Epoch 114/1000, Train Loss: 0.4906, Train Accuracy: 0.8859, Val Loss: 0.4920, Val Accuracy: 0.9072 Epoch 115/1000, Train Loss: 0.4886, Train Accuracy: 0.8871, Val Loss: 0.4889, Val Accuracy: 0.9081 Epoch 116/1000, Train Loss: 0.4866, Train Accuracy: 0.8882, Val Loss: 0.4859, Val Accuracy: 0.9087 Epoch 117/1000, Train Loss: 0.4821, Train Accuracy: 0.8898, Val Loss: 0.4828, Val Accuracy: 0.9087 Epoch 118/1000, Train Loss: 0.4792, Train Accuracy: 0.8908, Val Loss: 0.4797, Val Accuracy: 0.9087 Epoch 119/1000, Train Loss: 0.4760, Train Accuracy: 0.8919, Val Loss: 0.4766, Val Accuracy: 0.9084 Epoch 120/1000, Train Loss: 0.4731, Train Accuracy: 0.8892, Val Loss: 0.4734, Val Accuracy: 0.9087 Epoch 121/1000, Train Loss: 0.4697, Train Accuracy: 0.8915, Val Loss: 0.4702, Val Accuracy: 0.9095 Epoch 122/1000, Train Loss: 0.4663, Train Accuracy: 0.8947, Val Loss: 0.4670, Val Accuracy: 0.9098 Epoch 123/1000, Train Loss: 0.4639, Train Accuracy: 0.8921, Val Loss: 0.4638, Val Accuracy: 0.9110 Epoch 124/1000, Train Loss: 0.4598, Train Accuracy: 0.8957, Val Loss: 0.4606, Val Accuracy: 0.9113 Epoch 125/1000, Train Loss: 0.4577, Train Accuracy: 0.8944, Val Loss: 0.4574, Val Accuracy: 0.9127 Epoch 126/1000, Train Loss: 0.4540, Train Accuracy: 0.8940, Val Loss: 0.4542, Val Accuracy: 0.9127 Epoch 127/1000, Train Loss: 0.4528, Train Accuracy: 0.8960, Val Loss: 0.4509, Val Accuracy: 0.9127 Epoch 128/1000, Train Loss: 0.4479, Train Accuracy: 0.8993, Val Loss: 0.4477, Val Accuracy: 0.9142 Epoch 129/1000, Train Loss: 0.4439, Train Accuracy: 0.8990, Val Loss: 0.4444, Val Accuracy: 0.9145 Epoch 130/1000, Train Loss: 0.4425, Train Accuracy: 0.8974, Val Loss: 0.4412, Val Accuracy: 0.9153 Epoch 131/1000, Train Loss: 0.4391, Train Accuracy: 0.8959, Val Loss: 0.4379, Val Accuracy: 0.9153 Epoch 132/1000, Train Loss: 0.4363, Train Accuracy: 0.8979, Val Loss: 0.4346, Val Accuracy: 0.9171 Epoch 133/1000, Train Loss: 0.4329, Train Accuracy: 0.8995, Val Loss: 0.4312, Val Accuracy: 0.9171 Epoch 134/1000, Train Loss: 0.4292, Train Accuracy: 0.9029, Val Loss: 0.4281, Val Accuracy: 0.9182 Epoch 135/1000, Train Loss: 0.4261, Train Accuracy: 0.9017, Val Loss: 0.4248, Val Accuracy: 0.9185 Epoch 136/1000, Train Loss: 0.4234, Train Accuracy: 0.9001, Val Loss: 0.4215, Val Accuracy: 0.9191 Epoch 137/1000, Train Loss: 0.4200, Train Accuracy: 0.9017, Val Loss: 0.4182, Val Accuracy: 0.9199 Epoch 138/1000, Train Loss: 0.4162, Train Accuracy: 0.9028, Val Loss: 0.4149, Val Accuracy: 0.9199 Epoch 139/1000, Train Loss: 0.4136, Train Accuracy: 0.9022, Val Loss: 0.4116, Val Accuracy: 0.9208 Epoch 140/1000, Train Loss: 0.4094, Train Accuracy: 0.9046, Val Loss: 0.4083, Val Accuracy: 0.9217 Epoch 141/1000, Train Loss: 0.4070, Train Accuracy: 0.9048, Val Loss: 0.4049, Val Accuracy: 0.9220 Epoch 142/1000, Train Loss: 0.4049, Train Accuracy: 0.9031, Val Loss: 0.4016, Val Accuracy: 0.9225 Epoch 143/1000, Train Loss: 0.4015, Train Accuracy: 0.9027, Val Loss: 0.3983, Val Accuracy: 0.9231 Epoch 144/1000, Train Loss: 0.3989, Train Accuracy: 0.9059, Val Loss: 0.3951, Val Accuracy: 0.9231 Epoch 145/1000, Train Loss: 0.3937, Train Accuracy: 0.9092, Val Loss: 0.3918, Val Accuracy: 0.9237 Epoch 146/1000, Train Loss: 0.3917, Train Accuracy: 0.9081, Val Loss: 0.3886, Val Accuracy: 0.9243 Epoch 147/1000, Train Loss: 0.3885, Train Accuracy: 0.9083, Val Loss: 0.3853, Val Accuracy: 0.9249 Epoch 148/1000, Train Loss: 0.3867, Train Accuracy: 0.9087, Val Loss: 0.3820, Val Accuracy: 0.9251 Epoch 149/1000, Train Loss: 0.3827, Train Accuracy: 0.9102, Val Loss: 0.3788, Val Accuracy: 0.9254 Epoch 150/1000, Train Loss: 0.3784, Train Accuracy: 0.9103, Val Loss: 0.3756, Val Accuracy: 0.9254 Epoch 151/1000, Train Loss: 0.3763, Train Accuracy: 0.9114, Val Loss: 0.3725, Val Accuracy: 0.9257 Epoch 152/1000, Train Loss: 0.3724, Train Accuracy: 0.9116, Val Loss: 0.3693, Val Accuracy: 0.9260 Epoch 153/1000, Train Loss: 0.3701, Train Accuracy: 0.9089, Val Loss: 0.3662, Val Accuracy: 0.9263 Epoch 154/1000, Train Loss: 0.3663, Train Accuracy: 0.9130, Val Loss: 0.3631, Val Accuracy: 0.9275 Epoch 155/1000, Train Loss: 0.3647, Train Accuracy: 0.9108, Val Loss: 0.3600, Val Accuracy: 0.9277 Epoch 156/1000, Train Loss: 0.3607, Train Accuracy: 0.9134, Val Loss: 0.3570, Val Accuracy: 0.9286 Epoch 157/1000, Train Loss: 0.3563, Train Accuracy: 0.9133, Val Loss: 0.3539, Val Accuracy: 0.9283 Epoch 158/1000, Train Loss: 0.3545, Train Accuracy: 0.9149, Val Loss: 0.3510, Val Accuracy: 0.9283 Epoch 159/1000, Train Loss: 0.3534, Train Accuracy: 0.9129, Val Loss: 0.3480, Val Accuracy: 0.9292 Epoch 160/1000, Train Loss: 0.3496, Train Accuracy: 0.9135, Val Loss: 0.3450, Val Accuracy: 0.9292 Epoch 161/1000, Train Loss: 0.3478, Train Accuracy: 0.9137, Val Loss: 0.3421, Val Accuracy: 0.9303 Epoch 162/1000, Train Loss: 0.3447, Train Accuracy: 0.9174, Val Loss: 0.3391, Val Accuracy: 0.9309 Epoch 163/1000, Train Loss: 0.3401, Train Accuracy: 0.9173, Val Loss: 0.3361, Val Accuracy: 0.9312 Epoch 164/1000, Train Loss: 0.3386, Train Accuracy: 0.9180, Val Loss: 0.3332, Val Accuracy: 0.9324 Epoch 165/1000, Train Loss: 0.3366, Train Accuracy: 0.9159, Val Loss: 0.3303, Val Accuracy: 0.9327 Epoch 166/1000, Train Loss: 0.3326, Train Accuracy: 0.9199, Val Loss: 0.3274, Val Accuracy: 0.9329 Epoch 167/1000, Train Loss: 0.3303, Train Accuracy: 0.9163, Val Loss: 0.3246, Val Accuracy: 0.9335 Epoch 168/1000, Train Loss: 0.3259, Train Accuracy: 0.9200, Val Loss: 0.3217, Val Accuracy: 0.9344 Epoch 169/1000, Train Loss: 0.3246, Train Accuracy: 0.9219, Val Loss: 0.3190, Val Accuracy: 0.9350 Epoch 170/1000, Train Loss: 0.3231, Train Accuracy: 0.9199, Val Loss: 0.3163, Val Accuracy: 0.9350 Epoch 171/1000, Train Loss: 0.3210, Train Accuracy: 0.9181, Val Loss: 0.3136, Val Accuracy: 0.9353 Epoch 172/1000, Train Loss: 0.3161, Train Accuracy: 0.9225, Val Loss: 0.3108, Val Accuracy: 0.9355 Epoch 173/1000, Train Loss: 0.3130, Train Accuracy: 0.9236, Val Loss: 0.3081, Val Accuracy: 0.9355 Epoch 174/1000, Train Loss: 0.3118, Train Accuracy: 0.9198, Val Loss: 0.3055, Val Accuracy: 0.9364 Epoch 175/1000, Train Loss: 0.3086, Train Accuracy: 0.9246, Val Loss: 0.3029, Val Accuracy: 0.9370 Epoch 176/1000, Train Loss: 0.3075, Train Accuracy: 0.9265, Val Loss: 0.3002, Val Accuracy: 0.9373 Epoch 177/1000, Train Loss: 0.3044, Train Accuracy: 0.9242, Val Loss: 0.2977, Val Accuracy: 0.9376 Epoch 178/1000, Train Loss: 0.3016, Train Accuracy: 0.9244, Val Loss: 0.2952, Val Accuracy: 0.9387 Epoch 179/1000, Train Loss: 0.3003, Train Accuracy: 0.9248, Val Loss: 0.2927, Val Accuracy: 0.9390 Epoch 180/1000, Train Loss: 0.2962, Train Accuracy: 0.9270, Val Loss: 0.2902, Val Accuracy: 0.9393 Epoch 181/1000, Train Loss: 0.2921, Train Accuracy: 0.9244, Val Loss: 0.2878, Val Accuracy: 0.9396 Epoch 182/1000, Train Loss: 0.2924, Train Accuracy: 0.9272, Val Loss: 0.2852, Val Accuracy: 0.9408 Epoch 183/1000, Train Loss: 0.2891, Train Accuracy: 0.9279, Val Loss: 0.2829, Val Accuracy: 0.9402 Epoch 184/1000, Train Loss: 0.2873, Train Accuracy: 0.9270, Val Loss: 0.2805, Val Accuracy: 0.9408 Epoch 185/1000, Train Loss: 0.2852, Train Accuracy: 0.9272, Val Loss: 0.2781, Val Accuracy: 0.9405 Epoch 186/1000, Train Loss: 0.2830, Train Accuracy: 0.9288, Val Loss: 0.2758, Val Accuracy: 0.9408 Epoch 187/1000, Train Loss: 0.2803, Train Accuracy: 0.9275, Val Loss: 0.2736, Val Accuracy: 0.9416 Epoch 188/1000, Train Loss: 0.2784, Train Accuracy: 0.9274, Val Loss: 0.2714, Val Accuracy: 0.9419 Epoch 189/1000, Train Loss: 0.2749, Train Accuracy: 0.9289, Val Loss: 0.2691, Val Accuracy: 0.9422 Epoch 190/1000, Train Loss: 0.2730, Train Accuracy: 0.9307, Val Loss: 0.2669, Val Accuracy: 0.9428 Epoch 191/1000, Train Loss: 0.2717, Train Accuracy: 0.9305, Val Loss: 0.2647, Val Accuracy: 0.9425 Epoch 192/1000, Train Loss: 0.2683, Train Accuracy: 0.9329, Val Loss: 0.2626, Val Accuracy: 0.9425 Epoch 193/1000, Train Loss: 0.2686, Train Accuracy: 0.9311, Val Loss: 0.2604, Val Accuracy: 0.9425 Epoch 194/1000, Train Loss: 0.2643, Train Accuracy: 0.9323, Val Loss: 0.2583, Val Accuracy: 0.9428 Epoch 195/1000, Train Loss: 0.2626, Train Accuracy: 0.9330, Val Loss: 0.2562, Val Accuracy: 0.9428 Epoch 196/1000, Train Loss: 0.2604, Train Accuracy: 0.9331, Val Loss: 0.2541, Val Accuracy: 0.9434 Epoch 197/1000, Train Loss: 0.2601, Train Accuracy: 0.9345, Val Loss: 0.2520, Val Accuracy: 0.9434 Epoch 198/1000, Train Loss: 0.2567, Train Accuracy: 0.9332, Val Loss: 0.2499, Val Accuracy: 0.9434 Epoch 199/1000, Train Loss: 0.2556, Train Accuracy: 0.9329, Val Loss: 0.2480, Val Accuracy: 0.9445 Epoch 200/1000, Train Loss: 0.2527, Train Accuracy: 0.9355, Val Loss: 0.2460, Val Accuracy: 0.9457 Epoch 201/1000, Train Loss: 0.2496, Train Accuracy: 0.9337, Val Loss: 0.2440, Val Accuracy: 0.9457 Epoch 202/1000, Train Loss: 0.2486, Train Accuracy: 0.9358, Val Loss: 0.2421, Val Accuracy: 0.9462 Epoch 203/1000, Train Loss: 0.2475, Train Accuracy: 0.9368, Val Loss: 0.2401, Val Accuracy: 0.9465 Epoch 204/1000, Train Loss: 0.2452, Train Accuracy: 0.9351, Val Loss: 0.2383, Val Accuracy: 0.9457 Epoch 205/1000, Train Loss: 0.2439, Train Accuracy: 0.9340, Val Loss: 0.2365, Val Accuracy: 0.9462 Epoch 206/1000, Train Loss: 0.2415, Train Accuracy: 0.9394, Val Loss: 0.2347, Val Accuracy: 0.9465 Epoch 207/1000, Train Loss: 0.2384, Train Accuracy: 0.9367, Val Loss: 0.2329, Val Accuracy: 0.9465 Epoch 208/1000, Train Loss: 0.2385, Train Accuracy: 0.9382, Val Loss: 0.2311, Val Accuracy: 0.9468 Epoch 209/1000, Train Loss: 0.2354, Train Accuracy: 0.9398, Val Loss: 0.2293, Val Accuracy: 0.9471 Epoch 210/1000, Train Loss: 0.2358, Train Accuracy: 0.9371, Val Loss: 0.2275, Val Accuracy: 0.9474 Epoch 211/1000, Train Loss: 0.2323, Train Accuracy: 0.9404, Val Loss: 0.2258, Val Accuracy: 0.9474 Epoch 212/1000, Train Loss: 0.2313, Train Accuracy: 0.9423, Val Loss: 0.2241, Val Accuracy: 0.9474 Epoch 213/1000, Train Loss: 0.2302, Train Accuracy: 0.9389, Val Loss: 0.2224, Val Accuracy: 0.9474 Epoch 214/1000, Train Loss: 0.2268, Train Accuracy: 0.9400, Val Loss: 0.2207, Val Accuracy: 0.9474 Epoch 215/1000, Train Loss: 0.2261, Train Accuracy: 0.9405, Val Loss: 0.2191, Val Accuracy: 0.9474 Epoch 216/1000, Train Loss: 0.2246, Train Accuracy: 0.9401, Val Loss: 0.2175, Val Accuracy: 0.9474 Epoch 217/1000, Train Loss: 0.2220, Train Accuracy: 0.9434, Val Loss: 0.2159, Val Accuracy: 0.9471 Epoch 218/1000, Train Loss: 0.2204, Train Accuracy: 0.9420, Val Loss: 0.2142, Val Accuracy: 0.9471 Epoch 219/1000, Train Loss: 0.2206, Train Accuracy: 0.9411, Val Loss: 0.2127, Val Accuracy: 0.9471 Epoch 220/1000, Train Loss: 0.2190, Train Accuracy: 0.9410, Val Loss: 0.2111, Val Accuracy: 0.9474 Epoch 221/1000, Train Loss: 0.2162, Train Accuracy: 0.9436, Val Loss: 0.2096, Val Accuracy: 0.9480 Epoch 222/1000, Train Loss: 0.2145, Train Accuracy: 0.9441, Val Loss: 0.2081, Val Accuracy: 0.9480 Epoch 223/1000, Train Loss: 0.2139, Train Accuracy: 0.9413, Val Loss: 0.2066, Val Accuracy: 0.9480 Epoch 224/1000, Train Loss: 0.2142, Train Accuracy: 0.9423, Val Loss: 0.2052, Val Accuracy: 0.9480 Epoch 225/1000, Train Loss: 0.2113, Train Accuracy: 0.9431, Val Loss: 0.2037, Val Accuracy: 0.9480 Epoch 226/1000, Train Loss: 0.2078, Train Accuracy: 0.9438, Val Loss: 0.2023, Val Accuracy: 0.9483 Epoch 227/1000, Train Loss: 0.2077, Train Accuracy: 0.9448, Val Loss: 0.2009, Val Accuracy: 0.9483 Epoch 228/1000, Train Loss: 0.2054, Train Accuracy: 0.9451, Val Loss: 0.1995, Val Accuracy: 0.9488 Epoch 229/1000, Train Loss: 0.2051, Train Accuracy: 0.9452, Val Loss: 0.1981, Val Accuracy: 0.9491 Epoch 230/1000, Train Loss: 0.2030, Train Accuracy: 0.9463, Val Loss: 0.1967, Val Accuracy: 0.9494 Epoch 231/1000, Train Loss: 0.2019, Train Accuracy: 0.9443, Val Loss: 0.1954, Val Accuracy: 0.9494 Epoch 232/1000, Train Loss: 0.2006, Train Accuracy: 0.9454, Val Loss: 0.1941, Val Accuracy: 0.9494 Epoch 233/1000, Train Loss: 0.1986, Train Accuracy: 0.9476, Val Loss: 0.1927, Val Accuracy: 0.9494 Epoch 234/1000, Train Loss: 0.1982, Train Accuracy: 0.9475, Val Loss: 0.1914, Val Accuracy: 0.9497 Epoch 235/1000, Train Loss: 0.1962, Train Accuracy: 0.9468, Val Loss: 0.1902, Val Accuracy: 0.9500 Epoch 236/1000, Train Loss: 0.1946, Train Accuracy: 0.9493, Val Loss: 0.1890, Val Accuracy: 0.9500 Epoch 237/1000, Train Loss: 0.1938, Train Accuracy: 0.9467, Val Loss: 0.1877, Val Accuracy: 0.9506 Epoch 238/1000, Train Loss: 0.1924, Train Accuracy: 0.9483, Val Loss: 0.1865, Val Accuracy: 0.9506 Epoch 239/1000, Train Loss: 0.1912, Train Accuracy: 0.9474, Val Loss: 0.1853, Val Accuracy: 0.9509 Epoch 240/1000, Train Loss: 0.1909, Train Accuracy: 0.9478, Val Loss: 0.1841, Val Accuracy: 0.9512 Epoch 241/1000, Train Loss: 0.1913, Train Accuracy: 0.9488, Val Loss: 0.1830, Val Accuracy: 0.9509 Epoch 242/1000, Train Loss: 0.1881, Train Accuracy: 0.9489, Val Loss: 0.1818, Val Accuracy: 0.9512 Epoch 243/1000, Train Loss: 0.1870, Train Accuracy: 0.9485, Val Loss: 0.1807, Val Accuracy: 0.9512 Epoch 244/1000, Train Loss: 0.1844, Train Accuracy: 0.9519, Val Loss: 0.1795, Val Accuracy: 0.9514 Epoch 245/1000, Train Loss: 0.1860, Train Accuracy: 0.9485, Val Loss: 0.1783, Val Accuracy: 0.9520 Epoch 246/1000, Train Loss: 0.1823, Train Accuracy: 0.9507, Val Loss: 0.1772, Val Accuracy: 0.9526 Epoch 247/1000, Train Loss: 0.1822, Train Accuracy: 0.9493, Val Loss: 0.1761, Val Accuracy: 0.9529 Epoch 248/1000, Train Loss: 0.1809, Train Accuracy: 0.9511, Val Loss: 0.1750, Val Accuracy: 0.9529 Epoch 249/1000, Train Loss: 0.1794, Train Accuracy: 0.9519, Val Loss: 0.1739, Val Accuracy: 0.9532 Epoch 250/1000, Train Loss: 0.1793, Train Accuracy: 0.9504, Val Loss: 0.1728, Val Accuracy: 0.9532 Epoch 251/1000, Train Loss: 0.1776, Train Accuracy: 0.9517, Val Loss: 0.1718, Val Accuracy: 0.9532 Epoch 252/1000, Train Loss: 0.1773, Train Accuracy: 0.9522, Val Loss: 0.1706, Val Accuracy: 0.9535 Epoch 253/1000, Train Loss: 0.1765, Train Accuracy: 0.9504, Val Loss: 0.1696, Val Accuracy: 0.9538 Epoch 254/1000, Train Loss: 0.1739, Train Accuracy: 0.9524, Val Loss: 0.1687, Val Accuracy: 0.9543 Epoch 255/1000, Train Loss: 0.1746, Train Accuracy: 0.9522, Val Loss: 0.1676, Val Accuracy: 0.9546 Epoch 256/1000, Train Loss: 0.1729, Train Accuracy: 0.9525, Val Loss: 0.1666, Val Accuracy: 0.9549 Epoch 257/1000, Train Loss: 0.1704, Train Accuracy: 0.9540, Val Loss: 0.1656, Val Accuracy: 0.9549 Epoch 258/1000, Train Loss: 0.1703, Train Accuracy: 0.9545, Val Loss: 0.1647, Val Accuracy: 0.9555 Epoch 259/1000, Train Loss: 0.1692, Train Accuracy: 0.9520, Val Loss: 0.1637, Val Accuracy: 0.9552 Epoch 260/1000, Train Loss: 0.1684, Train Accuracy: 0.9532, Val Loss: 0.1627, Val Accuracy: 0.9552 Epoch 261/1000, Train Loss: 0.1689, Train Accuracy: 0.9532, Val Loss: 0.1617, Val Accuracy: 0.9555 Epoch 262/1000, Train Loss: 0.1670, Train Accuracy: 0.9532, Val Loss: 0.1608, Val Accuracy: 0.9558 Epoch 263/1000, Train Loss: 0.1631, Train Accuracy: 0.9546, Val Loss: 0.1599, Val Accuracy: 0.9564 Epoch 264/1000, Train Loss: 0.1641, Train Accuracy: 0.9547, Val Loss: 0.1590, Val Accuracy: 0.9564 Epoch 265/1000, Train Loss: 0.1631, Train Accuracy: 0.9557, Val Loss: 0.1581, Val Accuracy: 0.9564 Epoch 266/1000, Train Loss: 0.1633, Train Accuracy: 0.9558, Val Loss: 0.1571, Val Accuracy: 0.9561 Epoch 267/1000, Train Loss: 0.1616, Train Accuracy: 0.9559, Val Loss: 0.1562, Val Accuracy: 0.9561 Epoch 268/1000, Train Loss: 0.1616, Train Accuracy: 0.9556, Val Loss: 0.1554, Val Accuracy: 0.9561 Epoch 269/1000, Train Loss: 0.1588, Train Accuracy: 0.9571, Val Loss: 0.1544, Val Accuracy: 0.9561 Epoch 270/1000, Train Loss: 0.1579, Train Accuracy: 0.9562, Val Loss: 0.1536, Val Accuracy: 0.9561 Epoch 271/1000, Train Loss: 0.1578, Train Accuracy: 0.9568, Val Loss: 0.1527, Val Accuracy: 0.9561 Epoch 272/1000, Train Loss: 0.1575, Train Accuracy: 0.9553, Val Loss: 0.1520, Val Accuracy: 0.9564 Epoch 273/1000, Train Loss: 0.1559, Train Accuracy: 0.9574, Val Loss: 0.1511, Val Accuracy: 0.9564 Epoch 274/1000, Train Loss: 0.1554, Train Accuracy: 0.9561, Val Loss: 0.1503, Val Accuracy: 0.9564 Epoch 275/1000, Train Loss: 0.1526, Train Accuracy: 0.9587, Val Loss: 0.1495, Val Accuracy: 0.9564 Epoch 276/1000, Train Loss: 0.1525, Train Accuracy: 0.9579, Val Loss: 0.1487, Val Accuracy: 0.9566 Epoch 277/1000, Train Loss: 0.1542, Train Accuracy: 0.9582, Val Loss: 0.1479, Val Accuracy: 0.9569 Epoch 278/1000, Train Loss: 0.1520, Train Accuracy: 0.9579, Val Loss: 0.1470, Val Accuracy: 0.9572 Epoch 279/1000, Train Loss: 0.1516, Train Accuracy: 0.9565, Val Loss: 0.1463, Val Accuracy: 0.9578 Epoch 280/1000, Train Loss: 0.1521, Train Accuracy: 0.9565, Val Loss: 0.1455, Val Accuracy: 0.9578 Epoch 281/1000, Train Loss: 0.1499, Train Accuracy: 0.9585, Val Loss: 0.1448, Val Accuracy: 0.9578 Epoch 282/1000, Train Loss: 0.1493, Train Accuracy: 0.9592, Val Loss: 0.1440, Val Accuracy: 0.9578 Epoch 283/1000, Train Loss: 0.1473, Train Accuracy: 0.9586, Val Loss: 0.1432, Val Accuracy: 0.9578 Epoch 284/1000, Train Loss: 0.1486, Train Accuracy: 0.9570, Val Loss: 0.1425, Val Accuracy: 0.9578 Epoch 285/1000, Train Loss: 0.1482, Train Accuracy: 0.9590, Val Loss: 0.1418, Val Accuracy: 0.9581 Epoch 286/1000, Train Loss: 0.1457, Train Accuracy: 0.9591, Val Loss: 0.1411, Val Accuracy: 0.9584 Epoch 287/1000, Train Loss: 0.1463, Train Accuracy: 0.9581, Val Loss: 0.1403, Val Accuracy: 0.9584 Epoch 288/1000, Train Loss: 0.1448, Train Accuracy: 0.9579, Val Loss: 0.1397, Val Accuracy: 0.9584 Epoch 289/1000, Train Loss: 0.1437, Train Accuracy: 0.9590, Val Loss: 0.1390, Val Accuracy: 0.9584 Epoch 290/1000, Train Loss: 0.1417, Train Accuracy: 0.9613, Val Loss: 0.1384, Val Accuracy: 0.9587 Epoch 291/1000, Train Loss: 0.1431, Train Accuracy: 0.9600, Val Loss: 0.1377, Val Accuracy: 0.9590 Epoch 292/1000, Train Loss: 0.1410, Train Accuracy: 0.9619, Val Loss: 0.1371, Val Accuracy: 0.9592 Epoch 293/1000, Train Loss: 0.1412, Train Accuracy: 0.9600, Val Loss: 0.1364, Val Accuracy: 0.9595 Epoch 294/1000, Train Loss: 0.1397, Train Accuracy: 0.9607, Val Loss: 0.1356, Val Accuracy: 0.9595 Epoch 295/1000, Train Loss: 0.1388, Train Accuracy: 0.9616, Val Loss: 0.1350, Val Accuracy: 0.9598 Epoch 296/1000, Train Loss: 0.1382, Train Accuracy: 0.9611, Val Loss: 0.1343, Val Accuracy: 0.9598 Epoch 297/1000, Train Loss: 0.1368, Train Accuracy: 0.9618, Val Loss: 0.1336, Val Accuracy: 0.9601 Epoch 298/1000, Train Loss: 0.1388, Train Accuracy: 0.9605, Val Loss: 0.1330, Val Accuracy: 0.9607 Epoch 299/1000, Train Loss: 0.1387, Train Accuracy: 0.9608, Val Loss: 0.1324, Val Accuracy: 0.9613 Epoch 300/1000, Train Loss: 0.1379, Train Accuracy: 0.9617, Val Loss: 0.1319, Val Accuracy: 0.9610 Epoch 301/1000, Train Loss: 0.1353, Train Accuracy: 0.9598, Val Loss: 0.1314, Val Accuracy: 0.9610 Epoch 302/1000, Train Loss: 0.1352, Train Accuracy: 0.9604, Val Loss: 0.1307, Val Accuracy: 0.9613 Epoch 303/1000, Train Loss: 0.1339, Train Accuracy: 0.9607, Val Loss: 0.1301, Val Accuracy: 0.9616 Epoch 304/1000, Train Loss: 0.1354, Train Accuracy: 0.9603, Val Loss: 0.1295, Val Accuracy: 0.9616 Epoch 305/1000, Train Loss: 0.1331, Train Accuracy: 0.9625, Val Loss: 0.1289, Val Accuracy: 0.9618 Epoch 306/1000, Train Loss: 0.1322, Train Accuracy: 0.9636, Val Loss: 0.1283, Val Accuracy: 0.9618 Epoch 307/1000, Train Loss: 0.1328, Train Accuracy: 0.9611, Val Loss: 0.1278, Val Accuracy: 0.9616 Epoch 308/1000, Train Loss: 0.1315, Train Accuracy: 0.9614, Val Loss: 0.1272, Val Accuracy: 0.9616 Epoch 309/1000, Train Loss: 0.1304, Train Accuracy: 0.9645, Val Loss: 0.1265, Val Accuracy: 0.9618 Epoch 310/1000, Train Loss: 0.1297, Train Accuracy: 0.9629, Val Loss: 0.1259, Val Accuracy: 0.9618 Epoch 311/1000, Train Loss: 0.1303, Train Accuracy: 0.9619, Val Loss: 0.1252, Val Accuracy: 0.9618 Epoch 312/1000, Train Loss: 0.1273, Train Accuracy: 0.9645, Val Loss: 0.1247, Val Accuracy: 0.9621 Epoch 313/1000, Train Loss: 0.1286, Train Accuracy: 0.9619, Val Loss: 0.1241, Val Accuracy: 0.9624 Epoch 314/1000, Train Loss: 0.1284, Train Accuracy: 0.9626, Val Loss: 0.1237, Val Accuracy: 0.9627 Epoch 315/1000, Train Loss: 0.1277, Train Accuracy: 0.9635, Val Loss: 0.1231, Val Accuracy: 0.9627 Epoch 316/1000, Train Loss: 0.1286, Train Accuracy: 0.9640, Val Loss: 0.1225, Val Accuracy: 0.9627 Epoch 317/1000, Train Loss: 0.1275, Train Accuracy: 0.9626, Val Loss: 0.1220, Val Accuracy: 0.9627 Epoch 318/1000, Train Loss: 0.1269, Train Accuracy: 0.9629, Val Loss: 0.1216, Val Accuracy: 0.9627 Epoch 319/1000, Train Loss: 0.1241, Train Accuracy: 0.9650, Val Loss: 0.1211, Val Accuracy: 0.9627 Epoch 320/1000, Train Loss: 0.1240, Train Accuracy: 0.9639, Val Loss: 0.1206, Val Accuracy: 0.9627 Epoch 321/1000, Train Loss: 0.1258, Train Accuracy: 0.9632, Val Loss: 0.1201, Val Accuracy: 0.9627 Epoch 322/1000, Train Loss: 0.1238, Train Accuracy: 0.9650, Val Loss: 0.1196, Val Accuracy: 0.9630 Epoch 323/1000, Train Loss: 0.1242, Train Accuracy: 0.9639, Val Loss: 0.1192, Val Accuracy: 0.9630 Epoch 324/1000, Train Loss: 0.1199, Train Accuracy: 0.9649, Val Loss: 0.1186, Val Accuracy: 0.9627 Epoch 325/1000, Train Loss: 0.1220, Train Accuracy: 0.9650, Val Loss: 0.1182, Val Accuracy: 0.9627 Epoch 326/1000, Train Loss: 0.1229, Train Accuracy: 0.9646, Val Loss: 0.1177, Val Accuracy: 0.9630 Epoch 327/1000, Train Loss: 0.1227, Train Accuracy: 0.9641, Val Loss: 0.1172, Val Accuracy: 0.9630 Epoch 328/1000, Train Loss: 0.1197, Train Accuracy: 0.9652, Val Loss: 0.1167, Val Accuracy: 0.9627 Epoch 329/1000, Train Loss: 0.1203, Train Accuracy: 0.9654, Val Loss: 0.1163, Val Accuracy: 0.9630 Epoch 330/1000, Train Loss: 0.1212, Train Accuracy: 0.9638, Val Loss: 0.1158, Val Accuracy: 0.9633 Epoch 331/1000, Train Loss: 0.1205, Train Accuracy: 0.9649, Val Loss: 0.1153, Val Accuracy: 0.9642 Epoch 332/1000, Train Loss: 0.1181, Train Accuracy: 0.9657, Val Loss: 0.1148, Val Accuracy: 0.9642 Epoch 333/1000, Train Loss: 0.1192, Train Accuracy: 0.9639, Val Loss: 0.1144, Val Accuracy: 0.9645 Epoch 334/1000, Train Loss: 0.1161, Train Accuracy: 0.9673, Val Loss: 0.1140, Val Accuracy: 0.9645 Epoch 335/1000, Train Loss: 0.1166, Train Accuracy: 0.9659, Val Loss: 0.1136, Val Accuracy: 0.9645 Epoch 336/1000, Train Loss: 0.1166, Train Accuracy: 0.9647, Val Loss: 0.1132, Val Accuracy: 0.9645 Epoch 337/1000, Train Loss: 0.1155, Train Accuracy: 0.9665, Val Loss: 0.1128, Val Accuracy: 0.9645 Epoch 338/1000, Train Loss: 0.1148, Train Accuracy: 0.9667, Val Loss: 0.1124, Val Accuracy: 0.9642 Epoch 339/1000, Train Loss: 0.1165, Train Accuracy: 0.9668, Val Loss: 0.1119, Val Accuracy: 0.9645 Epoch 340/1000, Train Loss: 0.1147, Train Accuracy: 0.9670, Val Loss: 0.1115, Val Accuracy: 0.9650 Epoch 341/1000, Train Loss: 0.1164, Train Accuracy: 0.9653, Val Loss: 0.1109, Val Accuracy: 0.9647 Epoch 342/1000, Train Loss: 0.1139, Train Accuracy: 0.9678, Val Loss: 0.1107, Val Accuracy: 0.9650 Epoch 343/1000, Train Loss: 0.1126, Train Accuracy: 0.9663, Val Loss: 0.1102, Val Accuracy: 0.9650 Epoch 344/1000, Train Loss: 0.1136, Train Accuracy: 0.9664, Val Loss: 0.1098, Val Accuracy: 0.9650 Epoch 345/1000, Train Loss: 0.1147, Train Accuracy: 0.9659, Val Loss: 0.1094, Val Accuracy: 0.9650 Epoch 346/1000, Train Loss: 0.1132, Train Accuracy: 0.9665, Val Loss: 0.1090, Val Accuracy: 0.9653 Epoch 347/1000, Train Loss: 0.1105, Train Accuracy: 0.9681, Val Loss: 0.1086, Val Accuracy: 0.9653 Epoch 348/1000, Train Loss: 0.1111, Train Accuracy: 0.9676, Val Loss: 0.1083, Val Accuracy: 0.9659 Epoch 349/1000, Train Loss: 0.1105, Train Accuracy: 0.9677, Val Loss: 0.1079, Val Accuracy: 0.9659 Epoch 350/1000, Train Loss: 0.1114, Train Accuracy: 0.9674, Val Loss: 0.1074, Val Accuracy: 0.9659 Epoch 351/1000, Train Loss: 0.1097, Train Accuracy: 0.9671, Val Loss: 0.1071, Val Accuracy: 0.9659 Epoch 352/1000, Train Loss: 0.1128, Train Accuracy: 0.9663, Val Loss: 0.1068, Val Accuracy: 0.9659 Epoch 353/1000, Train Loss: 0.1098, Train Accuracy: 0.9677, Val Loss: 0.1066, Val Accuracy: 0.9662 Epoch 354/1000, Train Loss: 0.1103, Train Accuracy: 0.9676, Val Loss: 0.1062, Val Accuracy: 0.9668 Epoch 355/1000, Train Loss: 0.1083, Train Accuracy: 0.9675, Val Loss: 0.1058, Val Accuracy: 0.9668 Epoch 356/1000, Train Loss: 0.1092, Train Accuracy: 0.9676, Val Loss: 0.1054, Val Accuracy: 0.9662 Epoch 357/1000, Train Loss: 0.1066, Train Accuracy: 0.9681, Val Loss: 0.1050, Val Accuracy: 0.9665 Epoch 358/1000, Train Loss: 0.1076, Train Accuracy: 0.9682, Val Loss: 0.1047, Val Accuracy: 0.9665 Epoch 359/1000, Train Loss: 0.1056, Train Accuracy: 0.9684, Val Loss: 0.1044, Val Accuracy: 0.9665 Epoch 360/1000, Train Loss: 0.1062, Train Accuracy: 0.9685, Val Loss: 0.1040, Val Accuracy: 0.9665 Epoch 361/1000, Train Loss: 0.1081, Train Accuracy: 0.9673, Val Loss: 0.1037, Val Accuracy: 0.9671 Epoch 362/1000, Train Loss: 0.1071, Train Accuracy: 0.9675, Val Loss: 0.1033, Val Accuracy: 0.9673 Epoch 363/1000, Train Loss: 0.1046, Train Accuracy: 0.9689, Val Loss: 0.1030, Val Accuracy: 0.9676 Epoch 364/1000, Train Loss: 0.1037, Train Accuracy: 0.9706, Val Loss: 0.1026, Val Accuracy: 0.9676 Epoch 365/1000, Train Loss: 0.1046, Train Accuracy: 0.9687, Val Loss: 0.1023, Val Accuracy: 0.9676 Epoch 366/1000, Train Loss: 0.1051, Train Accuracy: 0.9687, Val Loss: 0.1019, Val Accuracy: 0.9676 Epoch 367/1000, Train Loss: 0.1040, Train Accuracy: 0.9688, Val Loss: 0.1015, Val Accuracy: 0.9679 Epoch 368/1000, Train Loss: 0.1031, Train Accuracy: 0.9695, Val Loss: 0.1014, Val Accuracy: 0.9676 Epoch 369/1000, Train Loss: 0.1027, Train Accuracy: 0.9689, Val Loss: 0.1010, Val Accuracy: 0.9676 Epoch 370/1000, Train Loss: 0.1041, Train Accuracy: 0.9693, Val Loss: 0.1007, Val Accuracy: 0.9679 Epoch 371/1000, Train Loss: 0.1038, Train Accuracy: 0.9694, Val Loss: 0.1005, Val Accuracy: 0.9679 Epoch 372/1000, Train Loss: 0.1023, Train Accuracy: 0.9696, Val Loss: 0.1002, Val Accuracy: 0.9685 Epoch 373/1000, Train Loss: 0.1022, Train Accuracy: 0.9704, Val Loss: 0.0998, Val Accuracy: 0.9682 Epoch 374/1000, Train Loss: 0.1015, Train Accuracy: 0.9710, Val Loss: 0.0995, Val Accuracy: 0.9679 Epoch 375/1000, Train Loss: 0.1015, Train Accuracy: 0.9704, Val Loss: 0.0993, Val Accuracy: 0.9679 Epoch 376/1000, Train Loss: 0.1012, Train Accuracy: 0.9697, Val Loss: 0.0990, Val Accuracy: 0.9682 Epoch 377/1000, Train Loss: 0.1006, Train Accuracy: 0.9698, Val Loss: 0.0986, Val Accuracy: 0.9679 Epoch 378/1000, Train Loss: 0.1015, Train Accuracy: 0.9686, Val Loss: 0.0985, Val Accuracy: 0.9682 Epoch 379/1000, Train Loss: 0.1004, Train Accuracy: 0.9702, Val Loss: 0.0981, Val Accuracy: 0.9682 Epoch 380/1000, Train Loss: 0.0999, Train Accuracy: 0.9693, Val Loss: 0.0978, Val Accuracy: 0.9685 Epoch 381/1000, Train Loss: 0.0988, Train Accuracy: 0.9712, Val Loss: 0.0976, Val Accuracy: 0.9682 Epoch 382/1000, Train Loss: 0.1006, Train Accuracy: 0.9696, Val Loss: 0.0972, Val Accuracy: 0.9682 Epoch 383/1000, Train Loss: 0.1004, Train Accuracy: 0.9694, Val Loss: 0.0970, Val Accuracy: 0.9685 Epoch 384/1000, Train Loss: 0.0984, Train Accuracy: 0.9704, Val Loss: 0.0967, Val Accuracy: 0.9688 Epoch 385/1000, Train Loss: 0.0985, Train Accuracy: 0.9717, Val Loss: 0.0965, Val Accuracy: 0.9682 Epoch 386/1000, Train Loss: 0.0991, Train Accuracy: 0.9696, Val Loss: 0.0962, Val Accuracy: 0.9685 Epoch 387/1000, Train Loss: 0.0980, Train Accuracy: 0.9703, Val Loss: 0.0960, Val Accuracy: 0.9682 Epoch 388/1000, Train Loss: 0.0975, Train Accuracy: 0.9705, Val Loss: 0.0957, Val Accuracy: 0.9688 Epoch 389/1000, Train Loss: 0.0969, Train Accuracy: 0.9716, Val Loss: 0.0956, Val Accuracy: 0.9688 Epoch 390/1000, Train Loss: 0.0962, Train Accuracy: 0.9708, Val Loss: 0.0953, Val Accuracy: 0.9691 Epoch 391/1000, Train Loss: 0.0986, Train Accuracy: 0.9686, Val Loss: 0.0952, Val Accuracy: 0.9691 Epoch 392/1000, Train Loss: 0.0966, Train Accuracy: 0.9710, Val Loss: 0.0948, Val Accuracy: 0.9691 Epoch 393/1000, Train Loss: 0.0970, Train Accuracy: 0.9717, Val Loss: 0.0945, Val Accuracy: 0.9691 Epoch 394/1000, Train Loss: 0.0953, Train Accuracy: 0.9714, Val Loss: 0.0942, Val Accuracy: 0.9691 Epoch 395/1000, Train Loss: 0.0956, Train Accuracy: 0.9727, Val Loss: 0.0939, Val Accuracy: 0.9694 Epoch 396/1000, Train Loss: 0.0958, Train Accuracy: 0.9724, Val Loss: 0.0937, Val Accuracy: 0.9694 Epoch 397/1000, Train Loss: 0.0948, Train Accuracy: 0.9715, Val Loss: 0.0934, Val Accuracy: 0.9694 Epoch 398/1000, Train Loss: 0.0949, Train Accuracy: 0.9704, Val Loss: 0.0931, Val Accuracy: 0.9694 Epoch 399/1000, Train Loss: 0.0960, Train Accuracy: 0.9700, Val Loss: 0.0929, Val Accuracy: 0.9697 Epoch 400/1000, Train Loss: 0.0936, Train Accuracy: 0.9733, Val Loss: 0.0926, Val Accuracy: 0.9697 Epoch 401/1000, Train Loss: 0.0953, Train Accuracy: 0.9715, Val Loss: 0.0923, Val Accuracy: 0.9697 Epoch 402/1000, Train Loss: 0.0934, Train Accuracy: 0.9726, Val Loss: 0.0921, Val Accuracy: 0.9697 Epoch 403/1000, Train Loss: 0.0959, Train Accuracy: 0.9712, Val Loss: 0.0919, Val Accuracy: 0.9697 Epoch 404/1000, Train Loss: 0.0925, Train Accuracy: 0.9721, Val Loss: 0.0917, Val Accuracy: 0.9697 Epoch 405/1000, Train Loss: 0.0926, Train Accuracy: 0.9720, Val Loss: 0.0915, Val Accuracy: 0.9694 Epoch 406/1000, Train Loss: 0.0918, Train Accuracy: 0.9722, Val Loss: 0.0912, Val Accuracy: 0.9697 Epoch 407/1000, Train Loss: 0.0937, Train Accuracy: 0.9711, Val Loss: 0.0910, Val Accuracy: 0.9699 Epoch 408/1000, Train Loss: 0.0929, Train Accuracy: 0.9707, Val Loss: 0.0909, Val Accuracy: 0.9697 Epoch 409/1000, Train Loss: 0.0932, Train Accuracy: 0.9717, Val Loss: 0.0906, Val Accuracy: 0.9697 Epoch 410/1000, Train Loss: 0.0909, Train Accuracy: 0.9732, Val Loss: 0.0905, Val Accuracy: 0.9697 Epoch 411/1000, Train Loss: 0.0920, Train Accuracy: 0.9713, Val Loss: 0.0902, Val Accuracy: 0.9697 Epoch 412/1000, Train Loss: 0.0918, Train Accuracy: 0.9727, Val Loss: 0.0900, Val Accuracy: 0.9699 Epoch 413/1000, Train Loss: 0.0918, Train Accuracy: 0.9708, Val Loss: 0.0899, Val Accuracy: 0.9697 Epoch 414/1000, Train Loss: 0.0920, Train Accuracy: 0.9720, Val Loss: 0.0895, Val Accuracy: 0.9697 Epoch 415/1000, Train Loss: 0.0911, Train Accuracy: 0.9728, Val Loss: 0.0893, Val Accuracy: 0.9697 Epoch 416/1000, Train Loss: 0.0912, Train Accuracy: 0.9710, Val Loss: 0.0892, Val Accuracy: 0.9699 Epoch 417/1000, Train Loss: 0.0902, Train Accuracy: 0.9723, Val Loss: 0.0891, Val Accuracy: 0.9697 Epoch 418/1000, Train Loss: 0.0900, Train Accuracy: 0.9719, Val Loss: 0.0891, Val Accuracy: 0.9694 Epoch 419/1000, Train Loss: 0.0898, Train Accuracy: 0.9730, Val Loss: 0.0888, Val Accuracy: 0.9694 Epoch 420/1000, Train Loss: 0.0891, Train Accuracy: 0.9732, Val Loss: 0.0885, Val Accuracy: 0.9697 Epoch 421/1000, Train Loss: 0.0881, Train Accuracy: 0.9728, Val Loss: 0.0882, Val Accuracy: 0.9697 Epoch 422/1000, Train Loss: 0.0889, Train Accuracy: 0.9730, Val Loss: 0.0879, Val Accuracy: 0.9694 Epoch 423/1000, Train Loss: 0.0873, Train Accuracy: 0.9738, Val Loss: 0.0877, Val Accuracy: 0.9691 Epoch 424/1000, Train Loss: 0.0882, Train Accuracy: 0.9732, Val Loss: 0.0875, Val Accuracy: 0.9691 Epoch 425/1000, Train Loss: 0.0883, Train Accuracy: 0.9730, Val Loss: 0.0872, Val Accuracy: 0.9691 Epoch 426/1000, Train Loss: 0.0874, Train Accuracy: 0.9735, Val Loss: 0.0871, Val Accuracy: 0.9691 Epoch 427/1000, Train Loss: 0.0871, Train Accuracy: 0.9735, Val Loss: 0.0869, Val Accuracy: 0.9691 Epoch 428/1000, Train Loss: 0.0881, Train Accuracy: 0.9729, Val Loss: 0.0867, Val Accuracy: 0.9694 Epoch 429/1000, Train Loss: 0.0877, Train Accuracy: 0.9725, Val Loss: 0.0866, Val Accuracy: 0.9697 Epoch 430/1000, Train Loss: 0.0877, Train Accuracy: 0.9738, Val Loss: 0.0865, Val Accuracy: 0.9699 Epoch 431/1000, Train Loss: 0.0865, Train Accuracy: 0.9731, Val Loss: 0.0864, Val Accuracy: 0.9697 Epoch 432/1000, Train Loss: 0.0869, Train Accuracy: 0.9717, Val Loss: 0.0862, Val Accuracy: 0.9697 Epoch 433/1000, Train Loss: 0.0866, Train Accuracy: 0.9730, Val Loss: 0.0859, Val Accuracy: 0.9697 Epoch 434/1000, Train Loss: 0.0845, Train Accuracy: 0.9748, Val Loss: 0.0856, Val Accuracy: 0.9699 Epoch 435/1000, Train Loss: 0.0866, Train Accuracy: 0.9736, Val Loss: 0.0855, Val Accuracy: 0.9699 Epoch 436/1000, Train Loss: 0.0867, Train Accuracy: 0.9736, Val Loss: 0.0852, Val Accuracy: 0.9699 Epoch 437/1000, Train Loss: 0.0849, Train Accuracy: 0.9746, Val Loss: 0.0851, Val Accuracy: 0.9699 Epoch 438/1000, Train Loss: 0.0859, Train Accuracy: 0.9741, Val Loss: 0.0850, Val Accuracy: 0.9705 Epoch 439/1000, Train Loss: 0.0861, Train Accuracy: 0.9729, Val Loss: 0.0848, Val Accuracy: 0.9705 Epoch 440/1000, Train Loss: 0.0839, Train Accuracy: 0.9743, Val Loss: 0.0845, Val Accuracy: 0.9705 Epoch 441/1000, Train Loss: 0.0866, Train Accuracy: 0.9728, Val Loss: 0.0845, Val Accuracy: 0.9708 Epoch 442/1000, Train Loss: 0.0851, Train Accuracy: 0.9743, Val Loss: 0.0843, Val Accuracy: 0.9708 Epoch 443/1000, Train Loss: 0.0848, Train Accuracy: 0.9746, Val Loss: 0.0842, Val Accuracy: 0.9708 Epoch 444/1000, Train Loss: 0.0833, Train Accuracy: 0.9747, Val Loss: 0.0840, Val Accuracy: 0.9714 Epoch 445/1000, Train Loss: 0.0840, Train Accuracy: 0.9736, Val Loss: 0.0838, Val Accuracy: 0.9711 Epoch 446/1000, Train Loss: 0.0843, Train Accuracy: 0.9738, Val Loss: 0.0837, Val Accuracy: 0.9714 Epoch 447/1000, Train Loss: 0.0833, Train Accuracy: 0.9743, Val Loss: 0.0835, Val Accuracy: 0.9714 Epoch 448/1000, Train Loss: 0.0848, Train Accuracy: 0.9734, Val Loss: 0.0834, Val Accuracy: 0.9711 Epoch 449/1000, Train Loss: 0.0848, Train Accuracy: 0.9734, Val Loss: 0.0833, Val Accuracy: 0.9708 Epoch 450/1000, Train Loss: 0.0856, Train Accuracy: 0.9725, Val Loss: 0.0832, Val Accuracy: 0.9714 Epoch 451/1000, Train Loss: 0.0827, Train Accuracy: 0.9741, Val Loss: 0.0830, Val Accuracy: 0.9711 Epoch 452/1000, Train Loss: 0.0840, Train Accuracy: 0.9729, Val Loss: 0.0830, Val Accuracy: 0.9714 Epoch 453/1000, Train Loss: 0.0818, Train Accuracy: 0.9752, Val Loss: 0.0828, Val Accuracy: 0.9714 Epoch 454/1000, Train Loss: 0.0826, Train Accuracy: 0.9733, Val Loss: 0.0827, Val Accuracy: 0.9714 Epoch 455/1000, Train Loss: 0.0821, Train Accuracy: 0.9738, Val Loss: 0.0824, Val Accuracy: 0.9711 Epoch 456/1000, Train Loss: 0.0822, Train Accuracy: 0.9743, Val Loss: 0.0823, Val Accuracy: 0.9714 Epoch 457/1000, Train Loss: 0.0822, Train Accuracy: 0.9735, Val Loss: 0.0820, Val Accuracy: 0.9714 Epoch 458/1000, Train Loss: 0.0831, Train Accuracy: 0.9746, Val Loss: 0.0818, Val Accuracy: 0.9714 Epoch 459/1000, Train Loss: 0.0809, Train Accuracy: 0.9736, Val Loss: 0.0817, Val Accuracy: 0.9714 Epoch 460/1000, Train Loss: 0.0817, Train Accuracy: 0.9743, Val Loss: 0.0815, Val Accuracy: 0.9714 Epoch 461/1000, Train Loss: 0.0808, Train Accuracy: 0.9741, Val Loss: 0.0815, Val Accuracy: 0.9714 Epoch 462/1000, Train Loss: 0.0800, Train Accuracy: 0.9746, Val Loss: 0.0812, Val Accuracy: 0.9714 Epoch 463/1000, Train Loss: 0.0802, Train Accuracy: 0.9738, Val Loss: 0.0811, Val Accuracy: 0.9714 Epoch 464/1000, Train Loss: 0.0815, Train Accuracy: 0.9746, Val Loss: 0.0809, Val Accuracy: 0.9711 Epoch 465/1000, Train Loss: 0.0814, Train Accuracy: 0.9741, Val Loss: 0.0809, Val Accuracy: 0.9711 Epoch 466/1000, Train Loss: 0.0820, Train Accuracy: 0.9738, Val Loss: 0.0809, Val Accuracy: 0.9714 Epoch 467/1000, Train Loss: 0.0797, Train Accuracy: 0.9764, Val Loss: 0.0807, Val Accuracy: 0.9714 Epoch 468/1000, Train Loss: 0.0812, Train Accuracy: 0.9740, Val Loss: 0.0805, Val Accuracy: 0.9711 Epoch 469/1000, Train Loss: 0.0803, Train Accuracy: 0.9748, Val Loss: 0.0804, Val Accuracy: 0.9711 Epoch 470/1000, Train Loss: 0.0807, Train Accuracy: 0.9748, Val Loss: 0.0802, Val Accuracy: 0.9711 Epoch 471/1000, Train Loss: 0.0781, Train Accuracy: 0.9760, Val Loss: 0.0801, Val Accuracy: 0.9714 Epoch 472/1000, Train Loss: 0.0795, Train Accuracy: 0.9755, Val Loss: 0.0799, Val Accuracy: 0.9714 Epoch 473/1000, Train Loss: 0.0789, Train Accuracy: 0.9769, Val Loss: 0.0798, Val Accuracy: 0.9714 Epoch 474/1000, Train Loss: 0.0778, Train Accuracy: 0.9751, Val Loss: 0.0796, Val Accuracy: 0.9714 Epoch 475/1000, Train Loss: 0.0796, Train Accuracy: 0.9750, Val Loss: 0.0794, Val Accuracy: 0.9714 Epoch 476/1000, Train Loss: 0.0782, Train Accuracy: 0.9746, Val Loss: 0.0793, Val Accuracy: 0.9717 Epoch 477/1000, Train Loss: 0.0788, Train Accuracy: 0.9754, Val Loss: 0.0792, Val Accuracy: 0.9717 Epoch 478/1000, Train Loss: 0.0783, Train Accuracy: 0.9759, Val Loss: 0.0792, Val Accuracy: 0.9717 Epoch 479/1000, Train Loss: 0.0790, Train Accuracy: 0.9748, Val Loss: 0.0790, Val Accuracy: 0.9717 Epoch 480/1000, Train Loss: 0.0774, Train Accuracy: 0.9762, Val Loss: 0.0789, Val Accuracy: 0.9717 Epoch 481/1000, Train Loss: 0.0775, Train Accuracy: 0.9754, Val Loss: 0.0788, Val Accuracy: 0.9717 Epoch 482/1000, Train Loss: 0.0781, Train Accuracy: 0.9766, Val Loss: 0.0786, Val Accuracy: 0.9717 Epoch 483/1000, Train Loss: 0.0766, Train Accuracy: 0.9767, Val Loss: 0.0785, Val Accuracy: 0.9717 Epoch 484/1000, Train Loss: 0.0765, Train Accuracy: 0.9759, Val Loss: 0.0784, Val Accuracy: 0.9717 Epoch 485/1000, Train Loss: 0.0778, Train Accuracy: 0.9751, Val Loss: 0.0783, Val Accuracy: 0.9717 Epoch 486/1000, Train Loss: 0.0775, Train Accuracy: 0.9757, Val Loss: 0.0782, Val Accuracy: 0.9717 Epoch 487/1000, Train Loss: 0.0767, Train Accuracy: 0.9764, Val Loss: 0.0781, Val Accuracy: 0.9717 Epoch 488/1000, Train Loss: 0.0774, Train Accuracy: 0.9759, Val Loss: 0.0779, Val Accuracy: 0.9717 Epoch 489/1000, Train Loss: 0.0761, Train Accuracy: 0.9766, Val Loss: 0.0778, Val Accuracy: 0.9717 Epoch 490/1000, Train Loss: 0.0786, Train Accuracy: 0.9743, Val Loss: 0.0778, Val Accuracy: 0.9717 Epoch 491/1000, Train Loss: 0.0771, Train Accuracy: 0.9772, Val Loss: 0.0776, Val Accuracy: 0.9717 Epoch 492/1000, Train Loss: 0.0767, Train Accuracy: 0.9757, Val Loss: 0.0775, Val Accuracy: 0.9720 Epoch 493/1000, Train Loss: 0.0760, Train Accuracy: 0.9756, Val Loss: 0.0773, Val Accuracy: 0.9717 Epoch 494/1000, Train Loss: 0.0772, Train Accuracy: 0.9757, Val Loss: 0.0773, Val Accuracy: 0.9717 Epoch 495/1000, Train Loss: 0.0769, Train Accuracy: 0.9755, Val Loss: 0.0771, Val Accuracy: 0.9717 Epoch 496/1000, Train Loss: 0.0767, Train Accuracy: 0.9755, Val Loss: 0.0769, Val Accuracy: 0.9717 Epoch 497/1000, Train Loss: 0.0738, Train Accuracy: 0.9771, Val Loss: 0.0769, Val Accuracy: 0.9717 Epoch 498/1000, Train Loss: 0.0742, Train Accuracy: 0.9772, Val Loss: 0.0768, Val Accuracy: 0.9717 Epoch 499/1000, Train Loss: 0.0748, Train Accuracy: 0.9759, Val Loss: 0.0766, Val Accuracy: 0.9717 Epoch 500/1000, Train Loss: 0.0764, Train Accuracy: 0.9759, Val Loss: 0.0765, Val Accuracy: 0.9717 Epoch 501/1000, Train Loss: 0.0753, Train Accuracy: 0.9769, Val Loss: 0.0764, Val Accuracy: 0.9717 Epoch 502/1000, Train Loss: 0.0755, Train Accuracy: 0.9759, Val Loss: 0.0763, Val Accuracy: 0.9717 Epoch 503/1000, Train Loss: 0.0750, Train Accuracy: 0.9764, Val Loss: 0.0762, Val Accuracy: 0.9717 Epoch 504/1000, Train Loss: 0.0726, Train Accuracy: 0.9773, Val Loss: 0.0761, Val Accuracy: 0.9717 Epoch 505/1000, Train Loss: 0.0742, Train Accuracy: 0.9763, Val Loss: 0.0759, Val Accuracy: 0.9717 Epoch 506/1000, Train Loss: 0.0738, Train Accuracy: 0.9766, Val Loss: 0.0756, Val Accuracy: 0.9720 Epoch 507/1000, Train Loss: 0.0753, Train Accuracy: 0.9762, Val Loss: 0.0757, Val Accuracy: 0.9717 Epoch 508/1000, Train Loss: 0.0752, Train Accuracy: 0.9771, Val Loss: 0.0758, Val Accuracy: 0.9717 Epoch 509/1000, Train Loss: 0.0743, Train Accuracy: 0.9768, Val Loss: 0.0757, Val Accuracy: 0.9717 Epoch 510/1000, Train Loss: 0.0741, Train Accuracy: 0.9759, Val Loss: 0.0755, Val Accuracy: 0.9717 Epoch 511/1000, Train Loss: 0.0724, Train Accuracy: 0.9766, Val Loss: 0.0753, Val Accuracy: 0.9725 Epoch 512/1000, Train Loss: 0.0723, Train Accuracy: 0.9783, Val Loss: 0.0753, Val Accuracy: 0.9725 Epoch 513/1000, Train Loss: 0.0749, Train Accuracy: 0.9767, Val Loss: 0.0752, Val Accuracy: 0.9725 Epoch 514/1000, Train Loss: 0.0737, Train Accuracy: 0.9760, Val Loss: 0.0752, Val Accuracy: 0.9723 Epoch 515/1000, Train Loss: 0.0734, Train Accuracy: 0.9762, Val Loss: 0.0750, Val Accuracy: 0.9725 Epoch 516/1000, Train Loss: 0.0712, Train Accuracy: 0.9776, Val Loss: 0.0750, Val Accuracy: 0.9725 Epoch 517/1000, Train Loss: 0.0718, Train Accuracy: 0.9774, Val Loss: 0.0749, Val Accuracy: 0.9720 Epoch 518/1000, Train Loss: 0.0725, Train Accuracy: 0.9773, Val Loss: 0.0747, Val Accuracy: 0.9725 Epoch 519/1000, Train Loss: 0.0719, Train Accuracy: 0.9754, Val Loss: 0.0745, Val Accuracy: 0.9725 Epoch 520/1000, Train Loss: 0.0724, Train Accuracy: 0.9781, Val Loss: 0.0743, Val Accuracy: 0.9725 Epoch 521/1000, Train Loss: 0.0738, Train Accuracy: 0.9764, Val Loss: 0.0743, Val Accuracy: 0.9728 Epoch 522/1000, Train Loss: 0.0725, Train Accuracy: 0.9762, Val Loss: 0.0742, Val Accuracy: 0.9728 Epoch 523/1000, Train Loss: 0.0720, Train Accuracy: 0.9770, Val Loss: 0.0741, Val Accuracy: 0.9725 Epoch 524/1000, Train Loss: 0.0720, Train Accuracy: 0.9779, Val Loss: 0.0740, Val Accuracy: 0.9725 Epoch 525/1000, Train Loss: 0.0719, Train Accuracy: 0.9783, Val Loss: 0.0739, Val Accuracy: 0.9728 Epoch 526/1000, Train Loss: 0.0710, Train Accuracy: 0.9778, Val Loss: 0.0739, Val Accuracy: 0.9725 Epoch 527/1000, Train Loss: 0.0714, Train Accuracy: 0.9784, Val Loss: 0.0738, Val Accuracy: 0.9725 Epoch 528/1000, Train Loss: 0.0728, Train Accuracy: 0.9759, Val Loss: 0.0735, Val Accuracy: 0.9728 Epoch 529/1000, Train Loss: 0.0717, Train Accuracy: 0.9768, Val Loss: 0.0735, Val Accuracy: 0.9725 Epoch 530/1000, Train Loss: 0.0706, Train Accuracy: 0.9775, Val Loss: 0.0736, Val Accuracy: 0.9728 Epoch 531/1000, Train Loss: 0.0706, Train Accuracy: 0.9785, Val Loss: 0.0734, Val Accuracy: 0.9728 Epoch 532/1000, Train Loss: 0.0711, Train Accuracy: 0.9775, Val Loss: 0.0735, Val Accuracy: 0.9728 Epoch 533/1000, Train Loss: 0.0697, Train Accuracy: 0.9773, Val Loss: 0.0734, Val Accuracy: 0.9728 Epoch 534/1000, Train Loss: 0.0713, Train Accuracy: 0.9769, Val Loss: 0.0732, Val Accuracy: 0.9728 Epoch 535/1000, Train Loss: 0.0715, Train Accuracy: 0.9774, Val Loss: 0.0730, Val Accuracy: 0.9731 Epoch 536/1000, Train Loss: 0.0718, Train Accuracy: 0.9771, Val Loss: 0.0729, Val Accuracy: 0.9731 Epoch 537/1000, Train Loss: 0.0724, Train Accuracy: 0.9767, Val Loss: 0.0727, Val Accuracy: 0.9731 Epoch 538/1000, Train Loss: 0.0687, Train Accuracy: 0.9785, Val Loss: 0.0727, Val Accuracy: 0.9731 Epoch 539/1000, Train Loss: 0.0702, Train Accuracy: 0.9771, Val Loss: 0.0727, Val Accuracy: 0.9728 Epoch 540/1000, Train Loss: 0.0700, Train Accuracy: 0.9781, Val Loss: 0.0727, Val Accuracy: 0.9725 Epoch 541/1000, Train Loss: 0.0708, Train Accuracy: 0.9771, Val Loss: 0.0726, Val Accuracy: 0.9725 Epoch 542/1000, Train Loss: 0.0690, Train Accuracy: 0.9788, Val Loss: 0.0726, Val Accuracy: 0.9725 Epoch 543/1000, Train Loss: 0.0705, Train Accuracy: 0.9779, Val Loss: 0.0724, Val Accuracy: 0.9728 Epoch 544/1000, Train Loss: 0.0709, Train Accuracy: 0.9774, Val Loss: 0.0723, Val Accuracy: 0.9728 Epoch 545/1000, Train Loss: 0.0678, Train Accuracy: 0.9777, Val Loss: 0.0722, Val Accuracy: 0.9728 Epoch 546/1000, Train Loss: 0.0681, Train Accuracy: 0.9782, Val Loss: 0.0721, Val Accuracy: 0.9734 Epoch 547/1000, Train Loss: 0.0693, Train Accuracy: 0.9788, Val Loss: 0.0719, Val Accuracy: 0.9737 Epoch 548/1000, Train Loss: 0.0697, Train Accuracy: 0.9775, Val Loss: 0.0718, Val Accuracy: 0.9737 Epoch 549/1000, Train Loss: 0.0690, Train Accuracy: 0.9765, Val Loss: 0.0717, Val Accuracy: 0.9737 Epoch 550/1000, Train Loss: 0.0688, Train Accuracy: 0.9777, Val Loss: 0.0717, Val Accuracy: 0.9737 Epoch 551/1000, Train Loss: 0.0678, Train Accuracy: 0.9778, Val Loss: 0.0716, Val Accuracy: 0.9737 Epoch 552/1000, Train Loss: 0.0697, Train Accuracy: 0.9775, Val Loss: 0.0716, Val Accuracy: 0.9734 Epoch 553/1000, Train Loss: 0.0676, Train Accuracy: 0.9788, Val Loss: 0.0716, Val Accuracy: 0.9731 Epoch 554/1000, Train Loss: 0.0703, Train Accuracy: 0.9773, Val Loss: 0.0716, Val Accuracy: 0.9728 Epoch 555/1000, Train Loss: 0.0687, Train Accuracy: 0.9780, Val Loss: 0.0715, Val Accuracy: 0.9731 Epoch 556/1000, Train Loss: 0.0680, Train Accuracy: 0.9782, Val Loss: 0.0713, Val Accuracy: 0.9734 Epoch 557/1000, Train Loss: 0.0687, Train Accuracy: 0.9779, Val Loss: 0.0713, Val Accuracy: 0.9734 Epoch 558/1000, Train Loss: 0.0699, Train Accuracy: 0.9790, Val Loss: 0.0714, Val Accuracy: 0.9731 Epoch 559/1000, Train Loss: 0.0668, Train Accuracy: 0.9781, Val Loss: 0.0713, Val Accuracy: 0.9734 Epoch 560/1000, Train Loss: 0.0687, Train Accuracy: 0.9772, Val Loss: 0.0711, Val Accuracy: 0.9734 Epoch 561/1000, Train Loss: 0.0673, Train Accuracy: 0.9779, Val Loss: 0.0711, Val Accuracy: 0.9731 Epoch 562/1000, Train Loss: 0.0662, Train Accuracy: 0.9784, Val Loss: 0.0710, Val Accuracy: 0.9734 Epoch 563/1000, Train Loss: 0.0681, Train Accuracy: 0.9778, Val Loss: 0.0709, Val Accuracy: 0.9734 Epoch 564/1000, Train Loss: 0.0673, Train Accuracy: 0.9780, Val Loss: 0.0708, Val Accuracy: 0.9734 Epoch 565/1000, Train Loss: 0.0695, Train Accuracy: 0.9766, Val Loss: 0.0706, Val Accuracy: 0.9734 Epoch 566/1000, Train Loss: 0.0671, Train Accuracy: 0.9779, Val Loss: 0.0706, Val Accuracy: 0.9731 Epoch 567/1000, Train Loss: 0.0656, Train Accuracy: 0.9785, Val Loss: 0.0705, Val Accuracy: 0.9734 Epoch 568/1000, Train Loss: 0.0678, Train Accuracy: 0.9778, Val Loss: 0.0705, Val Accuracy: 0.9731 Epoch 569/1000, Train Loss: 0.0661, Train Accuracy: 0.9793, Val Loss: 0.0703, Val Accuracy: 0.9728 Epoch 570/1000, Train Loss: 0.0672, Train Accuracy: 0.9782, Val Loss: 0.0703, Val Accuracy: 0.9728 Epoch 571/1000, Train Loss: 0.0664, Train Accuracy: 0.9785, Val Loss: 0.0703, Val Accuracy: 0.9728 Epoch 572/1000, Train Loss: 0.0671, Train Accuracy: 0.9790, Val Loss: 0.0702, Val Accuracy: 0.9728 Epoch 573/1000, Train Loss: 0.0660, Train Accuracy: 0.9793, Val Loss: 0.0701, Val Accuracy: 0.9728 Epoch 574/1000, Train Loss: 0.0657, Train Accuracy: 0.9780, Val Loss: 0.0700, Val Accuracy: 0.9728 Epoch 575/1000, Train Loss: 0.0659, Train Accuracy: 0.9793, Val Loss: 0.0699, Val Accuracy: 0.9731 Epoch 576/1000, Train Loss: 0.0672, Train Accuracy: 0.9788, Val Loss: 0.0698, Val Accuracy: 0.9734 Epoch 577/1000, Train Loss: 0.0647, Train Accuracy: 0.9799, Val Loss: 0.0700, Val Accuracy: 0.9728 Epoch 578/1000, Train Loss: 0.0666, Train Accuracy: 0.9795, Val Loss: 0.0699, Val Accuracy: 0.9728 Epoch 579/1000, Train Loss: 0.0677, Train Accuracy: 0.9794, Val Loss: 0.0698, Val Accuracy: 0.9728 Epoch 580/1000, Train Loss: 0.0661, Train Accuracy: 0.9788, Val Loss: 0.0698, Val Accuracy: 0.9731 Epoch 581/1000, Train Loss: 0.0657, Train Accuracy: 0.9788, Val Loss: 0.0698, Val Accuracy: 0.9728 Epoch 582/1000, Train Loss: 0.0668, Train Accuracy: 0.9788, Val Loss: 0.0697, Val Accuracy: 0.9728 Epoch 583/1000, Train Loss: 0.0662, Train Accuracy: 0.9791, Val Loss: 0.0697, Val Accuracy: 0.9725 Epoch 584/1000, Train Loss: 0.0654, Train Accuracy: 0.9793, Val Loss: 0.0696, Val Accuracy: 0.9728 Epoch 585/1000, Train Loss: 0.0676, Train Accuracy: 0.9786, Val Loss: 0.0694, Val Accuracy: 0.9731 Epoch 586/1000, Train Loss: 0.0655, Train Accuracy: 0.9790, Val Loss: 0.0693, Val Accuracy: 0.9731 Epoch 587/1000, Train Loss: 0.0640, Train Accuracy: 0.9803, Val Loss: 0.0692, Val Accuracy: 0.9728 Epoch 588/1000, Train Loss: 0.0649, Train Accuracy: 0.9795, Val Loss: 0.0690, Val Accuracy: 0.9734 Epoch 589/1000, Train Loss: 0.0642, Train Accuracy: 0.9806, Val Loss: 0.0689, Val Accuracy: 0.9734 Epoch 590/1000, Train Loss: 0.0657, Train Accuracy: 0.9790, Val Loss: 0.0691, Val Accuracy: 0.9728 Epoch 591/1000, Train Loss: 0.0630, Train Accuracy: 0.9805, Val Loss: 0.0690, Val Accuracy: 0.9731 Epoch 592/1000, Train Loss: 0.0652, Train Accuracy: 0.9801, Val Loss: 0.0690, Val Accuracy: 0.9728 Epoch 593/1000, Train Loss: 0.0657, Train Accuracy: 0.9792, Val Loss: 0.0690, Val Accuracy: 0.9728 Epoch 594/1000, Train Loss: 0.0649, Train Accuracy: 0.9789, Val Loss: 0.0687, Val Accuracy: 0.9734 Epoch 595/1000, Train Loss: 0.0663, Train Accuracy: 0.9793, Val Loss: 0.0687, Val Accuracy: 0.9737 Epoch 596/1000, Train Loss: 0.0658, Train Accuracy: 0.9785, Val Loss: 0.0685, Val Accuracy: 0.9734 Epoch 597/1000, Train Loss: 0.0640, Train Accuracy: 0.9801, Val Loss: 0.0684, Val Accuracy: 0.9734 Epoch 598/1000, Train Loss: 0.0627, Train Accuracy: 0.9790, Val Loss: 0.0685, Val Accuracy: 0.9734 Epoch 599/1000, Train Loss: 0.0652, Train Accuracy: 0.9789, Val Loss: 0.0683, Val Accuracy: 0.9734 Epoch 600/1000, Train Loss: 0.0632, Train Accuracy: 0.9793, Val Loss: 0.0684, Val Accuracy: 0.9734 Epoch 601/1000, Train Loss: 0.0629, Train Accuracy: 0.9791, Val Loss: 0.0682, Val Accuracy: 0.9734 Epoch 602/1000, Train Loss: 0.0644, Train Accuracy: 0.9800, Val Loss: 0.0682, Val Accuracy: 0.9734 Epoch 603/1000, Train Loss: 0.0637, Train Accuracy: 0.9789, Val Loss: 0.0681, Val Accuracy: 0.9734 Epoch 604/1000, Train Loss: 0.0637, Train Accuracy: 0.9792, Val Loss: 0.0682, Val Accuracy: 0.9731 Epoch 605/1000, Train Loss: 0.0643, Train Accuracy: 0.9793, Val Loss: 0.0680, Val Accuracy: 0.9734 Epoch 606/1000, Train Loss: 0.0644, Train Accuracy: 0.9798, Val Loss: 0.0680, Val Accuracy: 0.9731 Epoch 607/1000, Train Loss: 0.0617, Train Accuracy: 0.9797, Val Loss: 0.0681, Val Accuracy: 0.9728 Epoch 608/1000, Train Loss: 0.0633, Train Accuracy: 0.9792, Val Loss: 0.0678, Val Accuracy: 0.9734 Epoch 609/1000, Train Loss: 0.0617, Train Accuracy: 0.9796, Val Loss: 0.0678, Val Accuracy: 0.9734 Epoch 610/1000, Train Loss: 0.0642, Train Accuracy: 0.9799, Val Loss: 0.0677, Val Accuracy: 0.9734 Epoch 611/1000, Train Loss: 0.0651, Train Accuracy: 0.9790, Val Loss: 0.0677, Val Accuracy: 0.9734 Epoch 612/1000, Train Loss: 0.0610, Train Accuracy: 0.9807, Val Loss: 0.0676, Val Accuracy: 0.9731 Epoch 613/1000, Train Loss: 0.0630, Train Accuracy: 0.9782, Val Loss: 0.0676, Val Accuracy: 0.9731 Epoch 614/1000, Train Loss: 0.0630, Train Accuracy: 0.9793, Val Loss: 0.0674, Val Accuracy: 0.9734 Epoch 615/1000, Train Loss: 0.0633, Train Accuracy: 0.9787, Val Loss: 0.0676, Val Accuracy: 0.9734 Epoch 616/1000, Train Loss: 0.0629, Train Accuracy: 0.9803, Val Loss: 0.0675, Val Accuracy: 0.9737 Epoch 617/1000, Train Loss: 0.0638, Train Accuracy: 0.9792, Val Loss: 0.0676, Val Accuracy: 0.9737 Epoch 618/1000, Train Loss: 0.0629, Train Accuracy: 0.9801, Val Loss: 0.0676, Val Accuracy: 0.9737 Epoch 619/1000, Train Loss: 0.0607, Train Accuracy: 0.9806, Val Loss: 0.0675, Val Accuracy: 0.9737 Epoch 620/1000, Train Loss: 0.0623, Train Accuracy: 0.9802, Val Loss: 0.0674, Val Accuracy: 0.9737 Epoch 621/1000, Train Loss: 0.0610, Train Accuracy: 0.9802, Val Loss: 0.0673, Val Accuracy: 0.9740 Epoch 622/1000, Train Loss: 0.0613, Train Accuracy: 0.9811, Val Loss: 0.0673, Val Accuracy: 0.9740 Epoch 623/1000, Train Loss: 0.0635, Train Accuracy: 0.9786, Val Loss: 0.0671, Val Accuracy: 0.9743 Epoch 624/1000, Train Loss: 0.0598, Train Accuracy: 0.9814, Val Loss: 0.0669, Val Accuracy: 0.9743 Epoch 625/1000, Train Loss: 0.0607, Train Accuracy: 0.9809, Val Loss: 0.0670, Val Accuracy: 0.9737 Epoch 626/1000, Train Loss: 0.0612, Train Accuracy: 0.9801, Val Loss: 0.0669, Val Accuracy: 0.9737 Epoch 627/1000, Train Loss: 0.0610, Train Accuracy: 0.9799, Val Loss: 0.0669, Val Accuracy: 0.9740 Epoch 628/1000, Train Loss: 0.0611, Train Accuracy: 0.9803, Val Loss: 0.0667, Val Accuracy: 0.9740 Epoch 629/1000, Train Loss: 0.0606, Train Accuracy: 0.9801, Val Loss: 0.0669, Val Accuracy: 0.9743 Epoch 630/1000, Train Loss: 0.0614, Train Accuracy: 0.9798, Val Loss: 0.0667, Val Accuracy: 0.9743 Epoch 631/1000, Train Loss: 0.0614, Train Accuracy: 0.9796, Val Loss: 0.0667, Val Accuracy: 0.9746 Epoch 632/1000, Train Loss: 0.0612, Train Accuracy: 0.9803, Val Loss: 0.0668, Val Accuracy: 0.9746 Epoch 633/1000, Train Loss: 0.0609, Train Accuracy: 0.9806, Val Loss: 0.0667, Val Accuracy: 0.9749 Epoch 634/1000, Train Loss: 0.0613, Train Accuracy: 0.9807, Val Loss: 0.0666, Val Accuracy: 0.9749 Epoch 635/1000, Train Loss: 0.0611, Train Accuracy: 0.9802, Val Loss: 0.0665, Val Accuracy: 0.9749 Epoch 636/1000, Train Loss: 0.0620, Train Accuracy: 0.9795, Val Loss: 0.0664, Val Accuracy: 0.9749 Epoch 637/1000, Train Loss: 0.0604, Train Accuracy: 0.9803, Val Loss: 0.0665, Val Accuracy: 0.9749 Epoch 638/1000, Train Loss: 0.0619, Train Accuracy: 0.9784, Val Loss: 0.0663, Val Accuracy: 0.9749 Epoch 639/1000, Train Loss: 0.0596, Train Accuracy: 0.9808, Val Loss: 0.0662, Val Accuracy: 0.9749 Epoch 640/1000, Train Loss: 0.0595, Train Accuracy: 0.9811, Val Loss: 0.0662, Val Accuracy: 0.9749 Epoch 641/1000, Train Loss: 0.0620, Train Accuracy: 0.9787, Val Loss: 0.0663, Val Accuracy: 0.9749 Epoch 642/1000, Train Loss: 0.0602, Train Accuracy: 0.9817, Val Loss: 0.0662, Val Accuracy: 0.9749 Epoch 643/1000, Train Loss: 0.0605, Train Accuracy: 0.9799, Val Loss: 0.0662, Val Accuracy: 0.9749 Epoch 644/1000, Train Loss: 0.0596, Train Accuracy: 0.9814, Val Loss: 0.0660, Val Accuracy: 0.9749 Epoch 645/1000, Train Loss: 0.0597, Train Accuracy: 0.9801, Val Loss: 0.0662, Val Accuracy: 0.9751 Epoch 646/1000, Train Loss: 0.0605, Train Accuracy: 0.9809, Val Loss: 0.0662, Val Accuracy: 0.9751 Epoch 647/1000, Train Loss: 0.0607, Train Accuracy: 0.9803, Val Loss: 0.0661, Val Accuracy: 0.9751 Epoch 648/1000, Train Loss: 0.0611, Train Accuracy: 0.9800, Val Loss: 0.0659, Val Accuracy: 0.9749 Epoch 649/1000, Train Loss: 0.0615, Train Accuracy: 0.9801, Val Loss: 0.0659, Val Accuracy: 0.9749 Epoch 650/1000, Train Loss: 0.0587, Train Accuracy: 0.9819, Val Loss: 0.0659, Val Accuracy: 0.9751 Epoch 651/1000, Train Loss: 0.0602, Train Accuracy: 0.9813, Val Loss: 0.0658, Val Accuracy: 0.9751 Epoch 652/1000, Train Loss: 0.0605, Train Accuracy: 0.9801, Val Loss: 0.0658, Val Accuracy: 0.9751 Epoch 653/1000, Train Loss: 0.0592, Train Accuracy: 0.9812, Val Loss: 0.0657, Val Accuracy: 0.9751 Epoch 654/1000, Train Loss: 0.0580, Train Accuracy: 0.9822, Val Loss: 0.0659, Val Accuracy: 0.9754 Epoch 655/1000, Train Loss: 0.0573, Train Accuracy: 0.9808, Val Loss: 0.0658, Val Accuracy: 0.9754 Epoch 656/1000, Train Loss: 0.0593, Train Accuracy: 0.9810, Val Loss: 0.0655, Val Accuracy: 0.9751 Epoch 657/1000, Train Loss: 0.0610, Train Accuracy: 0.9800, Val Loss: 0.0654, Val Accuracy: 0.9751 Epoch 658/1000, Train Loss: 0.0590, Train Accuracy: 0.9806, Val Loss: 0.0654, Val Accuracy: 0.9751 Epoch 659/1000, Train Loss: 0.0566, Train Accuracy: 0.9824, Val Loss: 0.0654, Val Accuracy: 0.9751 Epoch 660/1000, Train Loss: 0.0597, Train Accuracy: 0.9817, Val Loss: 0.0656, Val Accuracy: 0.9754 Epoch 661/1000, Train Loss: 0.0601, Train Accuracy: 0.9800, Val Loss: 0.0653, Val Accuracy: 0.9751 Epoch 662/1000, Train Loss: 0.0583, Train Accuracy: 0.9803, Val Loss: 0.0655, Val Accuracy: 0.9754 Epoch 663/1000, Train Loss: 0.0586, Train Accuracy: 0.9813, Val Loss: 0.0654, Val Accuracy: 0.9754 Epoch 664/1000, Train Loss: 0.0586, Train Accuracy: 0.9813, Val Loss: 0.0654, Val Accuracy: 0.9754 Epoch 665/1000, Train Loss: 0.0583, Train Accuracy: 0.9811, Val Loss: 0.0652, Val Accuracy: 0.9754 Epoch 666/1000, Train Loss: 0.0579, Train Accuracy: 0.9808, Val Loss: 0.0653, Val Accuracy: 0.9754 Epoch 667/1000, Train Loss: 0.0592, Train Accuracy: 0.9806, Val Loss: 0.0652, Val Accuracy: 0.9754 Epoch 668/1000, Train Loss: 0.0597, Train Accuracy: 0.9801, Val Loss: 0.0654, Val Accuracy: 0.9754 Epoch 669/1000, Train Loss: 0.0581, Train Accuracy: 0.9806, Val Loss: 0.0653, Val Accuracy: 0.9754 Epoch 670/1000, Train Loss: 0.0567, Train Accuracy: 0.9813, Val Loss: 0.0653, Val Accuracy: 0.9757 Epoch 671/1000, Train Loss: 0.0566, Train Accuracy: 0.9814, Val Loss: 0.0652, Val Accuracy: 0.9754 Epoch 672/1000, Train Loss: 0.0578, Train Accuracy: 0.9811, Val Loss: 0.0653, Val Accuracy: 0.9754 Epoch 673/1000, Train Loss: 0.0568, Train Accuracy: 0.9816, Val Loss: 0.0651, Val Accuracy: 0.9754 Epoch 674/1000, Train Loss: 0.0570, Train Accuracy: 0.9820, Val Loss: 0.0649, Val Accuracy: 0.9757 Epoch 675/1000, Train Loss: 0.0570, Train Accuracy: 0.9816, Val Loss: 0.0650, Val Accuracy: 0.9754 Epoch 676/1000, Train Loss: 0.0558, Train Accuracy: 0.9824, Val Loss: 0.0649, Val Accuracy: 0.9754 Epoch 677/1000, Train Loss: 0.0595, Train Accuracy: 0.9805, Val Loss: 0.0647, Val Accuracy: 0.9754 Epoch 678/1000, Train Loss: 0.0557, Train Accuracy: 0.9825, Val Loss: 0.0648, Val Accuracy: 0.9754 Epoch 679/1000, Train Loss: 0.0556, Train Accuracy: 0.9823, Val Loss: 0.0648, Val Accuracy: 0.9754 Epoch 680/1000, Train Loss: 0.0572, Train Accuracy: 0.9813, Val Loss: 0.0649, Val Accuracy: 0.9760 Epoch 681/1000, Train Loss: 0.0557, Train Accuracy: 0.9810, Val Loss: 0.0650, Val Accuracy: 0.9757 Epoch 682/1000, Train Loss: 0.0547, Train Accuracy: 0.9817, Val Loss: 0.0648, Val Accuracy: 0.9760 Epoch 683/1000, Train Loss: 0.0556, Train Accuracy: 0.9825, Val Loss: 0.0644, Val Accuracy: 0.9760 Epoch 684/1000, Train Loss: 0.0597, Train Accuracy: 0.9798, Val Loss: 0.0645, Val Accuracy: 0.9763 Epoch 685/1000, Train Loss: 0.0570, Train Accuracy: 0.9811, Val Loss: 0.0646, Val Accuracy: 0.9763 Epoch 686/1000, Train Loss: 0.0568, Train Accuracy: 0.9807, Val Loss: 0.0644, Val Accuracy: 0.9763 Epoch 687/1000, Train Loss: 0.0564, Train Accuracy: 0.9818, Val Loss: 0.0643, Val Accuracy: 0.9763 Epoch 688/1000, Train Loss: 0.0573, Train Accuracy: 0.9816, Val Loss: 0.0643, Val Accuracy: 0.9763 Epoch 689/1000, Train Loss: 0.0531, Train Accuracy: 0.9834, Val Loss: 0.0643, Val Accuracy: 0.9763 Epoch 690/1000, Train Loss: 0.0549, Train Accuracy: 0.9819, Val Loss: 0.0644, Val Accuracy: 0.9763 Epoch 691/1000, Train Loss: 0.0562, Train Accuracy: 0.9817, Val Loss: 0.0642, Val Accuracy: 0.9763 Epoch 692/1000, Train Loss: 0.0546, Train Accuracy: 0.9824, Val Loss: 0.0643, Val Accuracy: 0.9763 Epoch 693/1000, Train Loss: 0.0564, Train Accuracy: 0.9816, Val Loss: 0.0641, Val Accuracy: 0.9763 Epoch 694/1000, Train Loss: 0.0545, Train Accuracy: 0.9829, Val Loss: 0.0640, Val Accuracy: 0.9760 Epoch 695/1000, Train Loss: 0.0571, Train Accuracy: 0.9809, Val Loss: 0.0641, Val Accuracy: 0.9760 Epoch 696/1000, Train Loss: 0.0572, Train Accuracy: 0.9809, Val Loss: 0.0641, Val Accuracy: 0.9760 Epoch 697/1000, Train Loss: 0.0553, Train Accuracy: 0.9814, Val Loss: 0.0641, Val Accuracy: 0.9763 Epoch 698/1000, Train Loss: 0.0574, Train Accuracy: 0.9814, Val Loss: 0.0642, Val Accuracy: 0.9763 Epoch 699/1000, Train Loss: 0.0563, Train Accuracy: 0.9814, Val Loss: 0.0641, Val Accuracy: 0.9763 Epoch 700/1000, Train Loss: 0.0544, Train Accuracy: 0.9823, Val Loss: 0.0640, Val Accuracy: 0.9763 Epoch 701/1000, Train Loss: 0.0554, Train Accuracy: 0.9824, Val Loss: 0.0640, Val Accuracy: 0.9763 Epoch 702/1000, Train Loss: 0.0550, Train Accuracy: 0.9816, Val Loss: 0.0640, Val Accuracy: 0.9763 Epoch 703/1000, Train Loss: 0.0555, Train Accuracy: 0.9829, Val Loss: 0.0639, Val Accuracy: 0.9763 Epoch 704/1000, Train Loss: 0.0544, Train Accuracy: 0.9829, Val Loss: 0.0638, Val Accuracy: 0.9763 Epoch 705/1000, Train Loss: 0.0563, Train Accuracy: 0.9809, Val Loss: 0.0637, Val Accuracy: 0.9763 Epoch 706/1000, Train Loss: 0.0533, Train Accuracy: 0.9837, Val Loss: 0.0638, Val Accuracy: 0.9763 Epoch 707/1000, Train Loss: 0.0544, Train Accuracy: 0.9826, Val Loss: 0.0635, Val Accuracy: 0.9766 Epoch 708/1000, Train Loss: 0.0553, Train Accuracy: 0.9825, Val Loss: 0.0637, Val Accuracy: 0.9763 Epoch 709/1000, Train Loss: 0.0552, Train Accuracy: 0.9816, Val Loss: 0.0634, Val Accuracy: 0.9766 Epoch 710/1000, Train Loss: 0.0555, Train Accuracy: 0.9825, Val Loss: 0.0635, Val Accuracy: 0.9766 Epoch 711/1000, Train Loss: 0.0539, Train Accuracy: 0.9817, Val Loss: 0.0635, Val Accuracy: 0.9766 Epoch 712/1000, Train Loss: 0.0566, Train Accuracy: 0.9816, Val Loss: 0.0633, Val Accuracy: 0.9766 Epoch 713/1000, Train Loss: 0.0526, Train Accuracy: 0.9832, Val Loss: 0.0633, Val Accuracy: 0.9766 Epoch 714/1000, Train Loss: 0.0540, Train Accuracy: 0.9829, Val Loss: 0.0634, Val Accuracy: 0.9769 Epoch 715/1000, Train Loss: 0.0543, Train Accuracy: 0.9820, Val Loss: 0.0634, Val Accuracy: 0.9769 Epoch 716/1000, Train Loss: 0.0549, Train Accuracy: 0.9822, Val Loss: 0.0633, Val Accuracy: 0.9769 Epoch 717/1000, Train Loss: 0.0542, Train Accuracy: 0.9819, Val Loss: 0.0633, Val Accuracy: 0.9769 Epoch 718/1000, Train Loss: 0.0550, Train Accuracy: 0.9819, Val Loss: 0.0631, Val Accuracy: 0.9766 Epoch 719/1000, Train Loss: 0.0541, Train Accuracy: 0.9826, Val Loss: 0.0633, Val Accuracy: 0.9763 Epoch 720/1000, Train Loss: 0.0545, Train Accuracy: 0.9823, Val Loss: 0.0633, Val Accuracy: 0.9766 Epoch 721/1000, Train Loss: 0.0523, Train Accuracy: 0.9837, Val Loss: 0.0633, Val Accuracy: 0.9766 Epoch 722/1000, Train Loss: 0.0551, Train Accuracy: 0.9816, Val Loss: 0.0631, Val Accuracy: 0.9766 Epoch 723/1000, Train Loss: 0.0527, Train Accuracy: 0.9837, Val Loss: 0.0630, Val Accuracy: 0.9766 Epoch 724/1000, Train Loss: 0.0538, Train Accuracy: 0.9826, Val Loss: 0.0631, Val Accuracy: 0.9763 Epoch 725/1000, Train Loss: 0.0534, Train Accuracy: 0.9830, Val Loss: 0.0633, Val Accuracy: 0.9760 Epoch 726/1000, Train Loss: 0.0538, Train Accuracy: 0.9829, Val Loss: 0.0633, Val Accuracy: 0.9760 Epoch 727/1000, Train Loss: 0.0522, Train Accuracy: 0.9829, Val Loss: 0.0632, Val Accuracy: 0.9763 Epoch 728/1000, Train Loss: 0.0545, Train Accuracy: 0.9821, Val Loss: 0.0631, Val Accuracy: 0.9763 Epoch 729/1000, Train Loss: 0.0548, Train Accuracy: 0.9818, Val Loss: 0.0629, Val Accuracy: 0.9763 Epoch 730/1000, Train Loss: 0.0534, Train Accuracy: 0.9830, Val Loss: 0.0631, Val Accuracy: 0.9760 Epoch 731/1000, Train Loss: 0.0546, Train Accuracy: 0.9816, Val Loss: 0.0628, Val Accuracy: 0.9760 Epoch 732/1000, Train Loss: 0.0529, Train Accuracy: 0.9833, Val Loss: 0.0630, Val Accuracy: 0.9760 Epoch 733/1000, Train Loss: 0.0549, Train Accuracy: 0.9815, Val Loss: 0.0631, Val Accuracy: 0.9757 Epoch 734/1000, Train Loss: 0.0532, Train Accuracy: 0.9828, Val Loss: 0.0631, Val Accuracy: 0.9757 Epoch 735/1000, Train Loss: 0.0520, Train Accuracy: 0.9823, Val Loss: 0.0629, Val Accuracy: 0.9760 Epoch 736/1000, Train Loss: 0.0532, Train Accuracy: 0.9835, Val Loss: 0.0631, Val Accuracy: 0.9757 Epoch 737/1000, Train Loss: 0.0535, Train Accuracy: 0.9832, Val Loss: 0.0629, Val Accuracy: 0.9760 Epoch 738/1000, Train Loss: 0.0527, Train Accuracy: 0.9824, Val Loss: 0.0629, Val Accuracy: 0.9760 Epoch 739/1000, Train Loss: 0.0530, Train Accuracy: 0.9830, Val Loss: 0.0629, Val Accuracy: 0.9760 Epoch 740/1000, Train Loss: 0.0537, Train Accuracy: 0.9834, Val Loss: 0.0631, Val Accuracy: 0.9760 Epoch 741/1000, Train Loss: 0.0538, Train Accuracy: 0.9823, Val Loss: 0.0628, Val Accuracy: 0.9760 Epoch 742/1000, Train Loss: 0.0524, Train Accuracy: 0.9836, Val Loss: 0.0628, Val Accuracy: 0.9763 Epoch 743/1000, Train Loss: 0.0539, Train Accuracy: 0.9829, Val Loss: 0.0627, Val Accuracy: 0.9763 Epoch 744/1000, Train Loss: 0.0528, Train Accuracy: 0.9837, Val Loss: 0.0624, Val Accuracy: 0.9760 Epoch 745/1000, Train Loss: 0.0544, Train Accuracy: 0.9821, Val Loss: 0.0625, Val Accuracy: 0.9760 Epoch 746/1000, Train Loss: 0.0529, Train Accuracy: 0.9828, Val Loss: 0.0625, Val Accuracy: 0.9763 Epoch 747/1000, Train Loss: 0.0518, Train Accuracy: 0.9830, Val Loss: 0.0627, Val Accuracy: 0.9760 Epoch 748/1000, Train Loss: 0.0531, Train Accuracy: 0.9824, Val Loss: 0.0626, Val Accuracy: 0.9763 Epoch 749/1000, Train Loss: 0.0527, Train Accuracy: 0.9829, Val Loss: 0.0623, Val Accuracy: 0.9766 Epoch 750/1000, Train Loss: 0.0536, Train Accuracy: 0.9826, Val Loss: 0.0624, Val Accuracy: 0.9769 Epoch 751/1000, Train Loss: 0.0525, Train Accuracy: 0.9833, Val Loss: 0.0623, Val Accuracy: 0.9766 Epoch 752/1000, Train Loss: 0.0543, Train Accuracy: 0.9822, Val Loss: 0.0625, Val Accuracy: 0.9769 Epoch 753/1000, Train Loss: 0.0547, Train Accuracy: 0.9822, Val Loss: 0.0626, Val Accuracy: 0.9769 Epoch 754/1000, Train Loss: 0.0513, Train Accuracy: 0.9834, Val Loss: 0.0624, Val Accuracy: 0.9769 Epoch 755/1000, Train Loss: 0.0525, Train Accuracy: 0.9830, Val Loss: 0.0625, Val Accuracy: 0.9766 Epoch 756/1000, Train Loss: 0.0523, Train Accuracy: 0.9835, Val Loss: 0.0623, Val Accuracy: 0.9766 Epoch 757/1000, Train Loss: 0.0524, Train Accuracy: 0.9829, Val Loss: 0.0624, Val Accuracy: 0.9769 Epoch 758/1000, Train Loss: 0.0519, Train Accuracy: 0.9832, Val Loss: 0.0623, Val Accuracy: 0.9769 Epoch 759/1000, Train Loss: 0.0526, Train Accuracy: 0.9821, Val Loss: 0.0620, Val Accuracy: 0.9769 Epoch 760/1000, Train Loss: 0.0507, Train Accuracy: 0.9844, Val Loss: 0.0620, Val Accuracy: 0.9769 Epoch 761/1000, Train Loss: 0.0541, Train Accuracy: 0.9829, Val Loss: 0.0618, Val Accuracy: 0.9769 Epoch 762/1000, Train Loss: 0.0521, Train Accuracy: 0.9827, Val Loss: 0.0618, Val Accuracy: 0.9772 Epoch 763/1000, Train Loss: 0.0523, Train Accuracy: 0.9829, Val Loss: 0.0620, Val Accuracy: 0.9769 Epoch 764/1000, Train Loss: 0.0521, Train Accuracy: 0.9827, Val Loss: 0.0620, Val Accuracy: 0.9775 Epoch 765/1000, Train Loss: 0.0521, Train Accuracy: 0.9833, Val Loss: 0.0620, Val Accuracy: 0.9777 Epoch 766/1000, Train Loss: 0.0516, Train Accuracy: 0.9825, Val Loss: 0.0620, Val Accuracy: 0.9775 Epoch 767/1000, Train Loss: 0.0508, Train Accuracy: 0.9841, Val Loss: 0.0621, Val Accuracy: 0.9777 Epoch 768/1000, Train Loss: 0.0521, Train Accuracy: 0.9829, Val Loss: 0.0620, Val Accuracy: 0.9772 Epoch 769/1000, Train Loss: 0.0518, Train Accuracy: 0.9826, Val Loss: 0.0619, Val Accuracy: 0.9772 Epoch 770/1000, Train Loss: 0.0522, Train Accuracy: 0.9822, Val Loss: 0.0620, Val Accuracy: 0.9772 Epoch 771/1000, Train Loss: 0.0525, Train Accuracy: 0.9842, Val Loss: 0.0620, Val Accuracy: 0.9772 Epoch 772/1000, Train Loss: 0.0503, Train Accuracy: 0.9834, Val Loss: 0.0619, Val Accuracy: 0.9772 Epoch 773/1000, Train Loss: 0.0519, Train Accuracy: 0.9841, Val Loss: 0.0621, Val Accuracy: 0.9772 Epoch 774/1000, Train Loss: 0.0495, Train Accuracy: 0.9851, Val Loss: 0.0621, Val Accuracy: 0.9772 Epoch 775/1000, Train Loss: 0.0503, Train Accuracy: 0.9837, Val Loss: 0.0618, Val Accuracy: 0.9769 Epoch 776/1000, Train Loss: 0.0488, Train Accuracy: 0.9841, Val Loss: 0.0618, Val Accuracy: 0.9769 Epoch 777/1000, Train Loss: 0.0512, Train Accuracy: 0.9832, Val Loss: 0.0616, Val Accuracy: 0.9769 Epoch 778/1000, Train Loss: 0.0513, Train Accuracy: 0.9826, Val Loss: 0.0616, Val Accuracy: 0.9772 Epoch 779/1000, Train Loss: 0.0514, Train Accuracy: 0.9829, Val Loss: 0.0614, Val Accuracy: 0.9772 Epoch 780/1000, Train Loss: 0.0500, Train Accuracy: 0.9834, Val Loss: 0.0616, Val Accuracy: 0.9772 Epoch 781/1000, Train Loss: 0.0485, Train Accuracy: 0.9846, Val Loss: 0.0614, Val Accuracy: 0.9772 Epoch 782/1000, Train Loss: 0.0496, Train Accuracy: 0.9840, Val Loss: 0.0616, Val Accuracy: 0.9769 Epoch 783/1000, Train Loss: 0.0496, Train Accuracy: 0.9844, Val Loss: 0.0617, Val Accuracy: 0.9772 Epoch 784/1000, Train Loss: 0.0515, Train Accuracy: 0.9832, Val Loss: 0.0616, Val Accuracy: 0.9772 Epoch 785/1000, Train Loss: 0.0494, Train Accuracy: 0.9842, Val Loss: 0.0615, Val Accuracy: 0.9772 Epoch 786/1000, Train Loss: 0.0507, Train Accuracy: 0.9835, Val Loss: 0.0617, Val Accuracy: 0.9772 Epoch 787/1000, Train Loss: 0.0497, Train Accuracy: 0.9839, Val Loss: 0.0616, Val Accuracy: 0.9772 Epoch 788/1000, Train Loss: 0.0493, Train Accuracy: 0.9835, Val Loss: 0.0615, Val Accuracy: 0.9772 Epoch 789/1000, Train Loss: 0.0493, Train Accuracy: 0.9839, Val Loss: 0.0612, Val Accuracy: 0.9772 Epoch 790/1000, Train Loss: 0.0515, Train Accuracy: 0.9837, Val Loss: 0.0615, Val Accuracy: 0.9772 Epoch 791/1000, Train Loss: 0.0487, Train Accuracy: 0.9844, Val Loss: 0.0614, Val Accuracy: 0.9772 Epoch 792/1000, Train Loss: 0.0495, Train Accuracy: 0.9835, Val Loss: 0.0613, Val Accuracy: 0.9772 Epoch 793/1000, Train Loss: 0.0503, Train Accuracy: 0.9835, Val Loss: 0.0613, Val Accuracy: 0.9772 Epoch 794/1000, Train Loss: 0.0496, Train Accuracy: 0.9843, Val Loss: 0.0610, Val Accuracy: 0.9772 Epoch 795/1000, Train Loss: 0.0479, Train Accuracy: 0.9853, Val Loss: 0.0611, Val Accuracy: 0.9772 Epoch 796/1000, Train Loss: 0.0499, Train Accuracy: 0.9840, Val Loss: 0.0612, Val Accuracy: 0.9772 Epoch 797/1000, Train Loss: 0.0491, Train Accuracy: 0.9850, Val Loss: 0.0611, Val Accuracy: 0.9772 Epoch 798/1000, Train Loss: 0.0504, Train Accuracy: 0.9825, Val Loss: 0.0611, Val Accuracy: 0.9775 Epoch 799/1000, Train Loss: 0.0502, Train Accuracy: 0.9832, Val Loss: 0.0611, Val Accuracy: 0.9775 Epoch 800/1000, Train Loss: 0.0491, Train Accuracy: 0.9848, Val Loss: 0.0610, Val Accuracy: 0.9775 Epoch 801/1000, Train Loss: 0.0483, Train Accuracy: 0.9845, Val Loss: 0.0611, Val Accuracy: 0.9775 Epoch 802/1000, Train Loss: 0.0483, Train Accuracy: 0.9842, Val Loss: 0.0613, Val Accuracy: 0.9772 Epoch 803/1000, Train Loss: 0.0514, Train Accuracy: 0.9834, Val Loss: 0.0611, Val Accuracy: 0.9775 Epoch 804/1000, Train Loss: 0.0497, Train Accuracy: 0.9845, Val Loss: 0.0607, Val Accuracy: 0.9777 Epoch 805/1000, Train Loss: 0.0496, Train Accuracy: 0.9843, Val Loss: 0.0610, Val Accuracy: 0.9775 Epoch 806/1000, Train Loss: 0.0494, Train Accuracy: 0.9839, Val Loss: 0.0610, Val Accuracy: 0.9772 Epoch 807/1000, Train Loss: 0.0474, Train Accuracy: 0.9854, Val Loss: 0.0609, Val Accuracy: 0.9775 Epoch 808/1000, Train Loss: 0.0480, Train Accuracy: 0.9855, Val Loss: 0.0610, Val Accuracy: 0.9775 Epoch 809/1000, Train Loss: 0.0494, Train Accuracy: 0.9850, Val Loss: 0.0610, Val Accuracy: 0.9775 Epoch 810/1000, Train Loss: 0.0468, Train Accuracy: 0.9850, Val Loss: 0.0612, Val Accuracy: 0.9775 Epoch 811/1000, Train Loss: 0.0476, Train Accuracy: 0.9848, Val Loss: 0.0613, Val Accuracy: 0.9769 Epoch 812/1000, Train Loss: 0.0488, Train Accuracy: 0.9848, Val Loss: 0.0608, Val Accuracy: 0.9775 Epoch 813/1000, Train Loss: 0.0473, Train Accuracy: 0.9840, Val Loss: 0.0608, Val Accuracy: 0.9775 Epoch 814/1000, Train Loss: 0.0473, Train Accuracy: 0.9853, Val Loss: 0.0610, Val Accuracy: 0.9775 Epoch 815/1000, Train Loss: 0.0488, Train Accuracy: 0.9850, Val Loss: 0.0609, Val Accuracy: 0.9775 Epoch 816/1000, Train Loss: 0.0483, Train Accuracy: 0.9844, Val Loss: 0.0608, Val Accuracy: 0.9777 Epoch 817/1000, Train Loss: 0.0486, Train Accuracy: 0.9843, Val Loss: 0.0605, Val Accuracy: 0.9777 Epoch 818/1000, Train Loss: 0.0466, Train Accuracy: 0.9850, Val Loss: 0.0607, Val Accuracy: 0.9777 Epoch 819/1000, Train Loss: 0.0487, Train Accuracy: 0.9840, Val Loss: 0.0606, Val Accuracy: 0.9777 Epoch 820/1000, Train Loss: 0.0492, Train Accuracy: 0.9832, Val Loss: 0.0604, Val Accuracy: 0.9777 Epoch 821/1000, Train Loss: 0.0473, Train Accuracy: 0.9840, Val Loss: 0.0604, Val Accuracy: 0.9777 Epoch 822/1000, Train Loss: 0.0479, Train Accuracy: 0.9846, Val Loss: 0.0606, Val Accuracy: 0.9777 Epoch 823/1000, Train Loss: 0.0474, Train Accuracy: 0.9851, Val Loss: 0.0609, Val Accuracy: 0.9777 Epoch 824/1000, Train Loss: 0.0498, Train Accuracy: 0.9836, Val Loss: 0.0606, Val Accuracy: 0.9777 Epoch 825/1000, Train Loss: 0.0471, Train Accuracy: 0.9849, Val Loss: 0.0605, Val Accuracy: 0.9777 Epoch 826/1000, Train Loss: 0.0476, Train Accuracy: 0.9845, Val Loss: 0.0606, Val Accuracy: 0.9777 Epoch 827/1000, Train Loss: 0.0463, Train Accuracy: 0.9850, Val Loss: 0.0606, Val Accuracy: 0.9777 Epoch 828/1000, Train Loss: 0.0476, Train Accuracy: 0.9846, Val Loss: 0.0608, Val Accuracy: 0.9777 Epoch 829/1000, Train Loss: 0.0451, Train Accuracy: 0.9859, Val Loss: 0.0606, Val Accuracy: 0.9777 Epoch 830/1000, Train Loss: 0.0475, Train Accuracy: 0.9842, Val Loss: 0.0604, Val Accuracy: 0.9777 Epoch 831/1000, Train Loss: 0.0465, Train Accuracy: 0.9849, Val Loss: 0.0604, Val Accuracy: 0.9777 Epoch 832/1000, Train Loss: 0.0475, Train Accuracy: 0.9855, Val Loss: 0.0604, Val Accuracy: 0.9777 Epoch 833/1000, Train Loss: 0.0475, Train Accuracy: 0.9844, Val Loss: 0.0604, Val Accuracy: 0.9777 Epoch 834/1000, Train Loss: 0.0480, Train Accuracy: 0.9853, Val Loss: 0.0606, Val Accuracy: 0.9777 Epoch 835/1000, Train Loss: 0.0477, Train Accuracy: 0.9848, Val Loss: 0.0603, Val Accuracy: 0.9777 Epoch 836/1000, Train Loss: 0.0457, Train Accuracy: 0.9850, Val Loss: 0.0604, Val Accuracy: 0.9777 Epoch 837/1000, Train Loss: 0.0467, Train Accuracy: 0.9850, Val Loss: 0.0603, Val Accuracy: 0.9777 Epoch 838/1000, Train Loss: 0.0453, Train Accuracy: 0.9865, Val Loss: 0.0606, Val Accuracy: 0.9777 Epoch 839/1000, Train Loss: 0.0461, Train Accuracy: 0.9853, Val Loss: 0.0601, Val Accuracy: 0.9777 Epoch 840/1000, Train Loss: 0.0460, Train Accuracy: 0.9857, Val Loss: 0.0602, Val Accuracy: 0.9777 Epoch 841/1000, Train Loss: 0.0479, Train Accuracy: 0.9846, Val Loss: 0.0603, Val Accuracy: 0.9777 Epoch 842/1000, Train Loss: 0.0464, Train Accuracy: 0.9850, Val Loss: 0.0603, Val Accuracy: 0.9777 Epoch 843/1000, Train Loss: 0.0485, Train Accuracy: 0.9837, Val Loss: 0.0603, Val Accuracy: 0.9777 Epoch 844/1000, Train Loss: 0.0446, Train Accuracy: 0.9863, Val Loss: 0.0601, Val Accuracy: 0.9777 Epoch 845/1000, Train Loss: 0.0484, Train Accuracy: 0.9840, Val Loss: 0.0603, Val Accuracy: 0.9777 Epoch 846/1000, Train Loss: 0.0461, Train Accuracy: 0.9845, Val Loss: 0.0603, Val Accuracy: 0.9777 Epoch 847/1000, Train Loss: 0.0480, Train Accuracy: 0.9839, Val Loss: 0.0600, Val Accuracy: 0.9780 Epoch 848/1000, Train Loss: 0.0477, Train Accuracy: 0.9842, Val Loss: 0.0602, Val Accuracy: 0.9780 Epoch 849/1000, Train Loss: 0.0458, Train Accuracy: 0.9859, Val Loss: 0.0600, Val Accuracy: 0.9780 Epoch 850/1000, Train Loss: 0.0462, Train Accuracy: 0.9850, Val Loss: 0.0602, Val Accuracy: 0.9780 Epoch 851/1000, Train Loss: 0.0457, Train Accuracy: 0.9847, Val Loss: 0.0599, Val Accuracy: 0.9780 Epoch 852/1000, Train Loss: 0.0469, Train Accuracy: 0.9839, Val Loss: 0.0599, Val Accuracy: 0.9780 Epoch 853/1000, Train Loss: 0.0440, Train Accuracy: 0.9866, Val Loss: 0.0597, Val Accuracy: 0.9780 Epoch 854/1000, Train Loss: 0.0471, Train Accuracy: 0.9849, Val Loss: 0.0597, Val Accuracy: 0.9780 Epoch 855/1000, Train Loss: 0.0461, Train Accuracy: 0.9855, Val Loss: 0.0597, Val Accuracy: 0.9780 Epoch 856/1000, Train Loss: 0.0467, Train Accuracy: 0.9848, Val Loss: 0.0596, Val Accuracy: 0.9780 Epoch 857/1000, Train Loss: 0.0466, Train Accuracy: 0.9851, Val Loss: 0.0596, Val Accuracy: 0.9780 Epoch 858/1000, Train Loss: 0.0452, Train Accuracy: 0.9853, Val Loss: 0.0595, Val Accuracy: 0.9777 Epoch 859/1000, Train Loss: 0.0455, Train Accuracy: 0.9858, Val Loss: 0.0595, Val Accuracy: 0.9780 Epoch 860/1000, Train Loss: 0.0474, Train Accuracy: 0.9837, Val Loss: 0.0594, Val Accuracy: 0.9780 Epoch 861/1000, Train Loss: 0.0460, Train Accuracy: 0.9844, Val Loss: 0.0599, Val Accuracy: 0.9780 Epoch 862/1000, Train Loss: 0.0448, Train Accuracy: 0.9855, Val Loss: 0.0597, Val Accuracy: 0.9777 Epoch 863/1000, Train Loss: 0.0460, Train Accuracy: 0.9851, Val Loss: 0.0595, Val Accuracy: 0.9780 Epoch 864/1000, Train Loss: 0.0443, Train Accuracy: 0.9863, Val Loss: 0.0598, Val Accuracy: 0.9777 Epoch 865/1000, Train Loss: 0.0458, Train Accuracy: 0.9852, Val Loss: 0.0598, Val Accuracy: 0.9777 Epoch 866/1000, Train Loss: 0.0444, Train Accuracy: 0.9860, Val Loss: 0.0595, Val Accuracy: 0.9777 Epoch 867/1000, Train Loss: 0.0453, Train Accuracy: 0.9845, Val Loss: 0.0593, Val Accuracy: 0.9777 Epoch 868/1000, Train Loss: 0.0433, Train Accuracy: 0.9868, Val Loss: 0.0594, Val Accuracy: 0.9780 Epoch 869/1000, Train Loss: 0.0467, Train Accuracy: 0.9850, Val Loss: 0.0594, Val Accuracy: 0.9780 Epoch 870/1000, Train Loss: 0.0463, Train Accuracy: 0.9857, Val Loss: 0.0597, Val Accuracy: 0.9780 Epoch 871/1000, Train Loss: 0.0472, Train Accuracy: 0.9839, Val Loss: 0.0595, Val Accuracy: 0.9780 Epoch 872/1000, Train Loss: 0.0466, Train Accuracy: 0.9848, Val Loss: 0.0596, Val Accuracy: 0.9780 Epoch 873/1000, Train Loss: 0.0453, Train Accuracy: 0.9850, Val Loss: 0.0595, Val Accuracy: 0.9780 Epoch 874/1000, Train Loss: 0.0433, Train Accuracy: 0.9867, Val Loss: 0.0595, Val Accuracy: 0.9780 Epoch 875/1000, Train Loss: 0.0462, Train Accuracy: 0.9841, Val Loss: 0.0593, Val Accuracy: 0.9780 Epoch 876/1000, Train Loss: 0.0432, Train Accuracy: 0.9863, Val Loss: 0.0594, Val Accuracy: 0.9780 Epoch 877/1000, Train Loss: 0.0442, Train Accuracy: 0.9855, Val Loss: 0.0595, Val Accuracy: 0.9780 Epoch 878/1000, Train Loss: 0.0434, Train Accuracy: 0.9858, Val Loss: 0.0595, Val Accuracy: 0.9777 Epoch 879/1000, Train Loss: 0.0435, Train Accuracy: 0.9873, Val Loss: 0.0594, Val Accuracy: 0.9780 Epoch 880/1000, Train Loss: 0.0458, Train Accuracy: 0.9856, Val Loss: 0.0594, Val Accuracy: 0.9780 Epoch 881/1000, Train Loss: 0.0447, Train Accuracy: 0.9860, Val Loss: 0.0593, Val Accuracy: 0.9780 Epoch 882/1000, Train Loss: 0.0449, Train Accuracy: 0.9863, Val Loss: 0.0592, Val Accuracy: 0.9780 Epoch 883/1000, Train Loss: 0.0438, Train Accuracy: 0.9863, Val Loss: 0.0594, Val Accuracy: 0.9780 Epoch 884/1000, Train Loss: 0.0441, Train Accuracy: 0.9855, Val Loss: 0.0593, Val Accuracy: 0.9777 Epoch 885/1000, Train Loss: 0.0445, Train Accuracy: 0.9858, Val Loss: 0.0593, Val Accuracy: 0.9777 Epoch 886/1000, Train Loss: 0.0449, Train Accuracy: 0.9850, Val Loss: 0.0592, Val Accuracy: 0.9780 Epoch 887/1000, Train Loss: 0.0449, Train Accuracy: 0.9857, Val Loss: 0.0591, Val Accuracy: 0.9780 Epoch 888/1000, Train Loss: 0.0450, Train Accuracy: 0.9850, Val Loss: 0.0593, Val Accuracy: 0.9780 Epoch 889/1000, Train Loss: 0.0437, Train Accuracy: 0.9861, Val Loss: 0.0595, Val Accuracy: 0.9777 Epoch 890/1000, Train Loss: 0.0452, Train Accuracy: 0.9856, Val Loss: 0.0592, Val Accuracy: 0.9780 Epoch 891/1000, Train Loss: 0.0453, Train Accuracy: 0.9844, Val Loss: 0.0595, Val Accuracy: 0.9777 Epoch 892/1000, Train Loss: 0.0436, Train Accuracy: 0.9863, Val Loss: 0.0591, Val Accuracy: 0.9780 Epoch 893/1000, Train Loss: 0.0446, Train Accuracy: 0.9863, Val Loss: 0.0593, Val Accuracy: 0.9780 Epoch 894/1000, Train Loss: 0.0448, Train Accuracy: 0.9853, Val Loss: 0.0592, Val Accuracy: 0.9780 Epoch 895/1000, Train Loss: 0.0436, Train Accuracy: 0.9863, Val Loss: 0.0592, Val Accuracy: 0.9780 Epoch 896/1000, Train Loss: 0.0447, Train Accuracy: 0.9861, Val Loss: 0.0592, Val Accuracy: 0.9780 Epoch 897/1000, Train Loss: 0.0432, Train Accuracy: 0.9855, Val Loss: 0.0591, Val Accuracy: 0.9780 Epoch 898/1000, Train Loss: 0.0439, Train Accuracy: 0.9853, Val Loss: 0.0590, Val Accuracy: 0.9780 Epoch 899/1000, Train Loss: 0.0429, Train Accuracy: 0.9861, Val Loss: 0.0591, Val Accuracy: 0.9780 Epoch 900/1000, Train Loss: 0.0445, Train Accuracy: 0.9858, Val Loss: 0.0591, Val Accuracy: 0.9780 Epoch 901/1000, Train Loss: 0.0439, Train Accuracy: 0.9856, Val Loss: 0.0589, Val Accuracy: 0.9780 Epoch 902/1000, Train Loss: 0.0426, Train Accuracy: 0.9872, Val Loss: 0.0588, Val Accuracy: 0.9780 Epoch 903/1000, Train Loss: 0.0442, Train Accuracy: 0.9848, Val Loss: 0.0589, Val Accuracy: 0.9780 Epoch 904/1000, Train Loss: 0.0424, Train Accuracy: 0.9861, Val Loss: 0.0590, Val Accuracy: 0.9780 Epoch 905/1000, Train Loss: 0.0435, Train Accuracy: 0.9865, Val Loss: 0.0591, Val Accuracy: 0.9780 Epoch 906/1000, Train Loss: 0.0434, Train Accuracy: 0.9866, Val Loss: 0.0589, Val Accuracy: 0.9780 Epoch 907/1000, Train Loss: 0.0433, Train Accuracy: 0.9857, Val Loss: 0.0588, Val Accuracy: 0.9780 Epoch 908/1000, Train Loss: 0.0417, Train Accuracy: 0.9874, Val Loss: 0.0592, Val Accuracy: 0.9780 Epoch 909/1000, Train Loss: 0.0438, Train Accuracy: 0.9860, Val Loss: 0.0589, Val Accuracy: 0.9780 Epoch 910/1000, Train Loss: 0.0430, Train Accuracy: 0.9857, Val Loss: 0.0591, Val Accuracy: 0.9780 Epoch 911/1000, Train Loss: 0.0440, Train Accuracy: 0.9863, Val Loss: 0.0589, Val Accuracy: 0.9780 Epoch 912/1000, Train Loss: 0.0436, Train Accuracy: 0.9854, Val Loss: 0.0588, Val Accuracy: 0.9777 Epoch 913/1000, Train Loss: 0.0409, Train Accuracy: 0.9868, Val Loss: 0.0586, Val Accuracy: 0.9777 Epoch 914/1000, Train Loss: 0.0427, Train Accuracy: 0.9859, Val Loss: 0.0585, Val Accuracy: 0.9777 Epoch 915/1000, Train Loss: 0.0449, Train Accuracy: 0.9846, Val Loss: 0.0586, Val Accuracy: 0.9780 Epoch 916/1000, Train Loss: 0.0422, Train Accuracy: 0.9859, Val Loss: 0.0589, Val Accuracy: 0.9780 Epoch 917/1000, Train Loss: 0.0429, Train Accuracy: 0.9866, Val Loss: 0.0588, Val Accuracy: 0.9777 Epoch 918/1000, Train Loss: 0.0437, Train Accuracy: 0.9861, Val Loss: 0.0589, Val Accuracy: 0.9777 Epoch 919/1000, Train Loss: 0.0440, Train Accuracy: 0.9861, Val Loss: 0.0588, Val Accuracy: 0.9777 Epoch 920/1000, Train Loss: 0.0431, Train Accuracy: 0.9857, Val Loss: 0.0588, Val Accuracy: 0.9780 Epoch 921/1000, Train Loss: 0.0441, Train Accuracy: 0.9855, Val Loss: 0.0588, Val Accuracy: 0.9780 Epoch 922/1000, Train Loss: 0.0407, Train Accuracy: 0.9874, Val Loss: 0.0588, Val Accuracy: 0.9780 Epoch 923/1000, Train Loss: 0.0422, Train Accuracy: 0.9871, Val Loss: 0.0588, Val Accuracy: 0.9780 Epoch 924/1000, Train Loss: 0.0420, Train Accuracy: 0.9868, Val Loss: 0.0590, Val Accuracy: 0.9780 Epoch 925/1000, Train Loss: 0.0410, Train Accuracy: 0.9882, Val Loss: 0.0588, Val Accuracy: 0.9780 Epoch 926/1000, Train Loss: 0.0430, Train Accuracy: 0.9868, Val Loss: 0.0589, Val Accuracy: 0.9780 Epoch 927/1000, Train Loss: 0.0431, Train Accuracy: 0.9866, Val Loss: 0.0589, Val Accuracy: 0.9780 Epoch 928/1000, Train Loss: 0.0393, Train Accuracy: 0.9875, Val Loss: 0.0588, Val Accuracy: 0.9783 Epoch 929/1000, Train Loss: 0.0432, Train Accuracy: 0.9861, Val Loss: 0.0589, Val Accuracy: 0.9783 Epoch 930/1000, Train Loss: 0.0422, Train Accuracy: 0.9862, Val Loss: 0.0588, Val Accuracy: 0.9783 Epoch 931/1000, Train Loss: 0.0421, Train Accuracy: 0.9863, Val Loss: 0.0587, Val Accuracy: 0.9783 Epoch 932/1000, Train Loss: 0.0425, Train Accuracy: 0.9868, Val Loss: 0.0588, Val Accuracy: 0.9786 Epoch 933/1000, Train Loss: 0.0434, Train Accuracy: 0.9856, Val Loss: 0.0586, Val Accuracy: 0.9786 Epoch 934/1000, Train Loss: 0.0422, Train Accuracy: 0.9868, Val Loss: 0.0585, Val Accuracy: 0.9783 Epoch 935/1000, Train Loss: 0.0418, Train Accuracy: 0.9860, Val Loss: 0.0587, Val Accuracy: 0.9786 Epoch 936/1000, Train Loss: 0.0420, Train Accuracy: 0.9868, Val Loss: 0.0587, Val Accuracy: 0.9783 Epoch 937/1000, Train Loss: 0.0413, Train Accuracy: 0.9874, Val Loss: 0.0586, Val Accuracy: 0.9783 Epoch 938/1000, Train Loss: 0.0423, Train Accuracy: 0.9872, Val Loss: 0.0583, Val Accuracy: 0.9783 Epoch 939/1000, Train Loss: 0.0399, Train Accuracy: 0.9869, Val Loss: 0.0584, Val Accuracy: 0.9783 Epoch 940/1000, Train Loss: 0.0415, Train Accuracy: 0.9869, Val Loss: 0.0589, Val Accuracy: 0.9780 Epoch 941/1000, Train Loss: 0.0433, Train Accuracy: 0.9863, Val Loss: 0.0586, Val Accuracy: 0.9780 Epoch 942/1000, Train Loss: 0.0395, Train Accuracy: 0.9869, Val Loss: 0.0586, Val Accuracy: 0.9780 Epoch 943/1000, Train Loss: 0.0423, Train Accuracy: 0.9862, Val Loss: 0.0585, Val Accuracy: 0.9783 Epoch 944/1000, Train Loss: 0.0414, Train Accuracy: 0.9874, Val Loss: 0.0584, Val Accuracy: 0.9783 Epoch 945/1000, Train Loss: 0.0418, Train Accuracy: 0.9866, Val Loss: 0.0584, Val Accuracy: 0.9783 Epoch 946/1000, Train Loss: 0.0412, Train Accuracy: 0.9863, Val Loss: 0.0584, Val Accuracy: 0.9786 Epoch 947/1000, Train Loss: 0.0425, Train Accuracy: 0.9858, Val Loss: 0.0585, Val Accuracy: 0.9780 Epoch 948/1000, Train Loss: 0.0410, Train Accuracy: 0.9879, Val Loss: 0.0584, Val Accuracy: 0.9780 Epoch 949/1000, Train Loss: 0.0402, Train Accuracy: 0.9875, Val Loss: 0.0586, Val Accuracy: 0.9777 Epoch 950/1000, Train Loss: 0.0414, Train Accuracy: 0.9867, Val Loss: 0.0585, Val Accuracy: 0.9780 Epoch 951/1000, Train Loss: 0.0407, Train Accuracy: 0.9866, Val Loss: 0.0585, Val Accuracy: 0.9777 Epoch 952/1000, Train Loss: 0.0422, Train Accuracy: 0.9864, Val Loss: 0.0584, Val Accuracy: 0.9783 Epoch 953/1000, Train Loss: 0.0413, Train Accuracy: 0.9868, Val Loss: 0.0583, Val Accuracy: 0.9783 Epoch 954/1000, Train Loss: 0.0404, Train Accuracy: 0.9877, Val Loss: 0.0584, Val Accuracy: 0.9780 Epoch 955/1000, Train Loss: 0.0418, Train Accuracy: 0.9869, Val Loss: 0.0581, Val Accuracy: 0.9780 Epoch 956/1000, Train Loss: 0.0405, Train Accuracy: 0.9873, Val Loss: 0.0582, Val Accuracy: 0.9780 Epoch 957/1000, Train Loss: 0.0415, Train Accuracy: 0.9876, Val Loss: 0.0583, Val Accuracy: 0.9777 Epoch 958/1000, Train Loss: 0.0406, Train Accuracy: 0.9871, Val Loss: 0.0583, Val Accuracy: 0.9780 Epoch 959/1000, Train Loss: 0.0413, Train Accuracy: 0.9867, Val Loss: 0.0582, Val Accuracy: 0.9780 Epoch 960/1000, Train Loss: 0.0410, Train Accuracy: 0.9869, Val Loss: 0.0584, Val Accuracy: 0.9780 Epoch 961/1000, Train Loss: 0.0394, Train Accuracy: 0.9874, Val Loss: 0.0584, Val Accuracy: 0.9780 Epoch 962/1000, Train Loss: 0.0404, Train Accuracy: 0.9871, Val Loss: 0.0585, Val Accuracy: 0.9780 Epoch 963/1000, Train Loss: 0.0407, Train Accuracy: 0.9876, Val Loss: 0.0582, Val Accuracy: 0.9780 Epoch 964/1000, Train Loss: 0.0413, Train Accuracy: 0.9871, Val Loss: 0.0582, Val Accuracy: 0.9780 Epoch 965/1000, Train Loss: 0.0414, Train Accuracy: 0.9869, Val Loss: 0.0583, Val Accuracy: 0.9780 Epoch 966/1000, Train Loss: 0.0416, Train Accuracy: 0.9874, Val Loss: 0.0582, Val Accuracy: 0.9780 Epoch 967/1000, Train Loss: 0.0393, Train Accuracy: 0.9876, Val Loss: 0.0581, Val Accuracy: 0.9780 Epoch 968/1000, Train Loss: 0.0394, Train Accuracy: 0.9874, Val Loss: 0.0584, Val Accuracy: 0.9783 Epoch 969/1000, Train Loss: 0.0397, Train Accuracy: 0.9870, Val Loss: 0.0585, Val Accuracy: 0.9783 Epoch 970/1000, Train Loss: 0.0409, Train Accuracy: 0.9866, Val Loss: 0.0581, Val Accuracy: 0.9783 Epoch 971/1000, Train Loss: 0.0372, Train Accuracy: 0.9887, Val Loss: 0.0585, Val Accuracy: 0.9783 Epoch 972/1000, Train Loss: 0.0393, Train Accuracy: 0.9880, Val Loss: 0.0583, Val Accuracy: 0.9783 Epoch 973/1000, Train Loss: 0.0408, Train Accuracy: 0.9867, Val Loss: 0.0584, Val Accuracy: 0.9783 Epoch 974/1000, Train Loss: 0.0412, Train Accuracy: 0.9876, Val Loss: 0.0581, Val Accuracy: 0.9783 Epoch 975/1000, Train Loss: 0.0415, Train Accuracy: 0.9866, Val Loss: 0.0581, Val Accuracy: 0.9783 Epoch 976/1000, Train Loss: 0.0398, Train Accuracy: 0.9876, Val Loss: 0.0579, Val Accuracy: 0.9786 Epoch 977/1000, Train Loss: 0.0395, Train Accuracy: 0.9877, Val Loss: 0.0577, Val Accuracy: 0.9786 Epoch 978/1000, Train Loss: 0.0398, Train Accuracy: 0.9882, Val Loss: 0.0579, Val Accuracy: 0.9780 Epoch 979/1000, Train Loss: 0.0369, Train Accuracy: 0.9889, Val Loss: 0.0578, Val Accuracy: 0.9783 Epoch 980/1000, Train Loss: 0.0390, Train Accuracy: 0.9872, Val Loss: 0.0583, Val Accuracy: 0.9780 Epoch 981/1000, Train Loss: 0.0401, Train Accuracy: 0.9869, Val Loss: 0.0581, Val Accuracy: 0.9780 Epoch 982/1000, Train Loss: 0.0395, Train Accuracy: 0.9873, Val Loss: 0.0580, Val Accuracy: 0.9780 Epoch 983/1000, Train Loss: 0.0403, Train Accuracy: 0.9880, Val Loss: 0.0583, Val Accuracy: 0.9780 Epoch 984/1000, Train Loss: 0.0390, Train Accuracy: 0.9875, Val Loss: 0.0582, Val Accuracy: 0.9780 Epoch 985/1000, Train Loss: 0.0387, Train Accuracy: 0.9877, Val Loss: 0.0582, Val Accuracy: 0.9780 Epoch 986/1000, Train Loss: 0.0405, Train Accuracy: 0.9872, Val Loss: 0.0583, Val Accuracy: 0.9780 Epoch 987/1000, Train Loss: 0.0375, Train Accuracy: 0.9877, Val Loss: 0.0578, Val Accuracy: 0.9780 Epoch 988/1000, Train Loss: 0.0375, Train Accuracy: 0.9887, Val Loss: 0.0578, Val Accuracy: 0.9780 Epoch 989/1000, Train Loss: 0.0385, Train Accuracy: 0.9871, Val Loss: 0.0580, Val Accuracy: 0.9780 Epoch 990/1000, Train Loss: 0.0409, Train Accuracy: 0.9869, Val Loss: 0.0580, Val Accuracy: 0.9783 Epoch 991/1000, Train Loss: 0.0390, Train Accuracy: 0.9875, Val Loss: 0.0580, Val Accuracy: 0.9783 Epoch 992/1000, Train Loss: 0.0383, Train Accuracy: 0.9879, Val Loss: 0.0578, Val Accuracy: 0.9786 Epoch 993/1000, Train Loss: 0.0386, Train Accuracy: 0.9884, Val Loss: 0.0580, Val Accuracy: 0.9783 Epoch 994/1000, Train Loss: 0.0373, Train Accuracy: 0.9885, Val Loss: 0.0579, Val Accuracy: 0.9783 Epoch 995/1000, Train Loss: 0.0387, Train Accuracy: 0.9874, Val Loss: 0.0580, Val Accuracy: 0.9783 Epoch 996/1000, Train Loss: 0.0400, Train Accuracy: 0.9871, Val Loss: 0.0581, Val Accuracy: 0.9783 Epoch 997/1000, Train Loss: 0.0389, Train Accuracy: 0.9882, Val Loss: 0.0579, Val Accuracy: 0.9783 Epoch 998/1000, Train Loss: 0.0382, Train Accuracy: 0.9878, Val Loss: 0.0579, Val Accuracy: 0.9783 Epoch 999/1000, Train Loss: 0.0384, Train Accuracy: 0.9882, Val Loss: 0.0579, Val Accuracy: 0.9783 Epoch 1000/1000, Train Loss: 0.0384, Train Accuracy: 0.9880, Val Loss: 0.0580, Val Accuracy: 0.9783
torch.save(model_DS_M.state_dict(), best_model_path_M)
# Create the neural network classifier
clf = MLPClassifier(random_state=42)
# Assuming you have performed a grid search and obtained the best estimator
# Define the parameter grid
param_grid = {
'hidden_layer_sizes': [(30,30), (10,10,10)],
'solver': ['adam', 'sgd'],
'alpha': [0.001, 0.01],
}
# Create the GridSearchCV object
grid_search = GridSearchCV(clf, param_grid, scoring='accuracy', cv=2)
# Fit the GridSearchCV object to the data
grid_search.fit(X_train_DS_MCF7_nn, y_train_DS_MCF7_nn)
# Save the best estimator in best_nn
best_nn_DS_MCF7 = grid_search.best_estimator_
print(best_nn_DS_MCF7)
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn(
MLPClassifier(alpha=0.01, hidden_layer_sizes=(30, 30), random_state=42,
solver='sgd')
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn(
¶
Test for the model drop-seq MCF7
This below is the step for testing our neural network model.
First, it is set to evaluation mode to ensure consistency during the evaluation process, and test data is converted to tensors (again to be able to work with PyTorch).
A forward pass is performed on the test data to obtain predictions. The test loss is calculated using the same loss function (BCEWithLogitsLoss) and predictions are converted to binary values by applying a sigmoid function. Finally, the number of correct predictions is counted and used to compute the test accuracy.
# Load the best model
model_DS_M.load_state_dict(torch.load('best_model_final_M.pth'))
# Evaluate on test data
model_DS_M.eval()
X_test_tensor_M = torch.tensor(X_test_df_DS_MCF7.values, dtype=torch.float32)
y_test_tensor_M = torch.tensor(y_test_df_DS_MCF7.values, dtype=torch.float32).unsqueeze(1)
with torch.no_grad():
test_output = model_DS_M(X_test_tensor_M)
test_loss = criterion(test_output, y_test_tensor_M)
test_predictions = torch.round(torch.sigmoid(test_output))
test_correct = (test_predictions == y_test_tensor_M).sum().item()
test_total = y_test_tensor_M.size(0)
test_acc = test_correct / test_total
print(f'Test Loss: {test_loss.item():.4f}, Test Accuracy: {test_acc:.4f}')
# Test Loss: 0.0537, Test Accuracy: 0.9804
Test Loss: 0.0570, Test Accuracy: 0.9794
¶
4.3.5) KNN
best_knn_DS_MCF7=train_and_evaluate_knn(X_train_DS_MCF, X_test_DS_MCF, y_train_DS_MCF, y_test_DS_MCF)
Best hyperparameters: {'n_neighbors': 2}
Accuracy: 0.5305131761442441
F1: 0.6015303119482048
Classification Report:
precision recall f1-score support
0.0 0.77 0.30 0.43 2565
1.0 0.46 0.87 0.60 1761
accuracy 0.53 4326
macro avg 0.61 0.58 0.52 4326
weighted avg 0.64 0.53 0.50 4326
¶
4.3.6) Logistic Regression
best_logreg_DS_MCF7=train_and_evaluate_logreg(X_train_DS_MCF, X_test_DS_MCF, y_train_DS_MCF, y_test_DS_MCF)
Best hyperparameters: {'C': 0.002, 'penalty': 'l2'}
Accuracy: 0.9759593157651411
F1: 0.9701321079839172
Classification Report:
precision recall f1-score support
0.0 0.97 0.99 0.98 2565
1.0 0.98 0.96 0.97 1761
accuracy 0.98 4326
macro avg 0.98 0.97 0.98 4326
weighted avg 0.98 0.98 0.98 4326
# try pca method to reduce number of features and use log log regression
X_train_pca_DS_MCF, X_test_pca_DS_MCF, y_train_DS_MCF, y_test_DS_MCF,scaler_DS_MCF,pca_DS_MCF = prepare_data_pca(df_trans_DS_MCF)
X_train_pca_DS_MCF.shape
(17300, 1869)
model_logreg_mc_DROP_ver2_DS_MCF=train_and_evaluate_logreg(X_train_pca_DS_MCF, X_test_pca_DS_MCF, y_train_DS_MCF, y_test_DS_MCF)
Best hyperparameters: {'C': 0.002, 'penalty': 'l2'}
Accuracy: 0.9759593157651411
F1: 0.9701492537313432
Classification Report:
precision recall f1-score support
0.0 0.97 0.99 0.98 2565
1.0 0.98 0.96 0.97 1761
accuracy 0.98 4326
macro avg 0.98 0.97 0.98 4326
weighted avg 0.98 0.98 0.98 4326
¶
4.3.7) CatBoost
best_catboost_DS_MCF7=train_and_evaluate_catboost(X_train_DS_MCF, X_test_DS_MCF, y_train_DS_MCF, y_test_DS_MCF)
Best hyperparameters: {'logging_level': 'Silent'}
Accuracy: 0.9854368932038835
F1: 0.9820051413881747
Classification Report:
precision recall f1-score support
0.0 0.98 0.99 0.99 2565
1.0 0.99 0.98 0.98 1761
accuracy 0.99 4326
macro avg 0.99 0.98 0.98 4326
weighted avg 0.99 0.99 0.99 4326
new_columns_names_DS_MCF = []
for i in range(1,3001):
name_new_DS_MCF = f'f{i}'
new_columns_names_DS_MCF.append(name_new_DS_MCF)
X_test_pd_DS_MCF=pd.DataFrame(X_test_DS_MCF, columns=new_columns_names_DS_MCF)
X_train_pd_DS_MCF=pd.DataFrame(X_train_DS_MCF, columns=new_columns_names_DS_MCF)
model_catboost_mc_DROP_ver2_DS_MCF,cols_cut_DS_MCF=train_and_evaluate_catboost2(X_train_pd_DS_MCF, X_test_pd_DS_MCF, y_train_DS_MCF, y_test_DS_MCF)
185
Best hyperparameters: {'l2_leaf_reg': 3, 'logging_level': 'Silent', 'n_estimators': 1500}
Accuracy: 0.9870550161812298
F1: 0.984
Classification Report:
precision recall f1-score support
0.0 0.98 0.99 0.99 2565
1.0 0.99 0.98 0.98 1761
accuracy 0.99 4326
macro avg 0.99 0.99 0.99 4326
weighted avg 0.99 0.99 0.99 4326
¶
4.4) PREDICTORS DROPSEQ HCC1806
We apply the same steps of the dataset before (see 4.1) to the DropSeq HCC1806 dataset.
#load the DropSeq HCC1806 dataset
df_DS_HCC1806_norm_filt = pd.read_csv("DropSeq\HCC1806_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)
df_DS_HCC1806_norm_filtT = df_DS_HCC1806_norm_filt.T
# Add the target column
df_DS_HCC1806_norm_filtT['target'] = np.where(df_DS_HCC1806_norm_filtT.index.str.contains('Hypo'), 1, 0)
#i want to split the data into training and testing data, with a balance of 70% training and 30% testing
X_df_DS_HCC1806 = df_DS_HCC1806_norm_filtT.drop('target', axis=1)
y_df_DS_HCC1806 = df_DS_HCC1806_norm_filtT['target']
X_train_df_DS_HCC1806, X_test_df_DS_HCC1806, y_train_df_DS_HCC1806, y_test_df_DS_HCC1806 = train_test_split(X_df_DS_HCC1806, y_df_DS_HCC1806, test_size=0.3, random_state=42)
df_basic_DS_HCC=df_DS_HCC1806_norm_filt
df_trans_DS_HCC=df_basic_DS_HCC.T
df_trans_DS_HCC['TARGET'] = df_trans_DS_HCC.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_DS_HCC.loc[df_trans_DS_HCC.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_DS_HCC.loc[df_trans_DS_HCC.index.str.contains('Hypo'), 'TARGET'] = 1
X_train_DS_HCC, X_test_DS_HCC, y_train_DS_HCC, y_test_DS_HCC,scaler_DS_HCC = prepare_data(df_trans_DS_HCC)
¶
4.4.1) Support Vector Machine
We used same approach used for SmartSeq MCF7, see 4.1.1.
# build SVM on DropSeq HCC1806
# Define the SVM classifier
svm = SVC(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'kernel': ['linear', 'rbf']}
grid_search_svm = GridSearchCV(svm, param_grid, cv=2)
# Define the leave one out cross validation
#loo = LeaveOneOut()
# Perform the nested cross validation
scores_svm = cross_val_score(grid_search_svm, X_train_df_DS_HCC1806, y_train_df_DS_HCC1806, cv=2)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of 2 fold cross validation: ", np.mean(scores_svm))
Mean accuracy of 2 fold cross validation: 0.9504724478872324
# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search_svm.fit(X_train_df_DS_HCC1806, y_train_df_DS_HCC1806)
print("Best kernel: ", grid_search_svm.best_params_)
print("Best accuracy: ", grid_search_svm.best_score_)
best_svm_DS_HCC1806 = grid_search_svm.best_estimator_
print("Best model: ", best_svm_DS_HCC1806)
# Test the model on the test dataset
print("Test accuracy: ", best_svm_DS_HCC1806.score(X_test_df_DS_HCC1806, y_test_df_DS_HCC1806))
Best kernel: {'kernel': 'rbf'}
Best accuracy: 0.9504724478872324
Best model: SVC(random_state=42)
Test accuracy: 0.9584562996594779
¶
4.4.2) Random Forest
Here we build the Random forest classifier, the strategy is the same as the other DropSeq dataset.
In this case the hypeparameters are slightly higher in the grid search but the cross validation confirms that there is no overfitting.
The performance in this dataset is the lowest among all the others, it gets a score of 92,14%.
The impressive thing in this case is that all the four genes we spotted in the exploration of the dataset (BCYRN1, RPL41, HSP90AA1, MALAT1) are present in the most important feature ranking, and this was very interesting as it showed that we were able, without further informations, to find some genes that at the end resulted very important for the predictions.
#i build the random forest classifier for the DropSeq HCC1806 dataset
# Define the random forest classifier
rf = RandomForestClassifier(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [100, 150], 'max_depth': [40, 50, 70]}
grid_search = GridSearchCV(rf, param_grid, cv=5)
outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)
# Perform the cross validation
scores = cross_val_score(grid_search, X_train_df_DS_HCC1806, y_train_df_DS_HCC1806, cv=outer_cv)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))
# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_DS_HCC1806, y_train_df_DS_HCC1806)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)
#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_DS_HCC1806, y_test_df_DS_HCC1806))
# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_DS_HCC1806.shape[1]):
print("%d. feature %s (%f)" % (f + 1, X_train_df_DS_HCC1806.columns[indices[f]], importances[indices[f]]))
best_rf_DS_MCF7 = grid_search.best_estimator_
Mean accuracy of nested cross validation: 0.9303298399083568
Best hyperparameters: {'max_depth': 70, 'n_estimators': 150}
Best accuracy: 0.9302334630350195
Best model: RandomForestClassifier(max_depth=70, n_estimators=150, random_state=42)
Test accuracy: 0.9343927355278093
Feature ranking:
1. feature "NDRG1" (0.023911)
2. feature "AKR1C2" (0.023133)
3. feature "BCYRN1" (0.019082)
4. feature "IGFBP3" (0.018349)
5. feature "FGF23" (0.016695)
6. feature "HSP90AA1" (0.016592)
7. feature "LDHA" (0.016076)
8. feature "AKR1C1" (0.015522)
9. feature "HSPD1" (0.015154)
10. feature "GPM6A" (0.013662)
11. feature "WDR43" (0.012692)
12. feature "EIF3J" (0.011704)
13. feature "NCL" (0.010339)
14. feature "RPL41" (0.010255)
15. feature "P4HA1" (0.009400)
16. feature "TMSB10" (0.009368)
17. feature "HSP90AB1" (0.008751)
18. feature "CALM2" (0.008416)
19. feature "S100A10" (0.008073)
20. feature "RPS28" (0.007900)
21. feature "MALAT1" (0.007849)
22. feature "ENO1" (0.007802)
23. feature "NPM1P40" (0.007409)
24. feature "RPS27" (0.007406)
25. feature "BNIP3" (0.007076)
26. feature "HSP90B1" (0.005948)
27. feature "KRT19" (0.005588)
28. feature "B4GALT1" (0.005297)
29. feature "RPS5" (0.005291)
30. feature "PGK1" (0.005287)
31. feature "ZC3H15" (0.005089)
32. feature "FTL" (0.005048)
33. feature "H4C3" (0.004805)
34. feature "NCALD" (0.004597)
35. feature "YWHAZ" (0.004507)
36. feature "S100A11" (0.004499)
37. feature "CACNA1A" (0.004492)
38. feature "PSMA7" (0.004460)
39. feature "HNRNPU" (0.004389)
40. feature "SERF2" (0.004206)
41. feature "BTBD9" (0.004205)
42. feature "RPL35" (0.004187)
43. feature "RPLP1" (0.004092)
44. feature "ZNF302" (0.003777)
45. feature "NEAT1" (0.003733)
46. feature "MT-RNR2" (0.003594)
47. feature "RPL39" (0.003581)
48. feature "CAST" (0.003562)
49. feature "EEF2" (0.003488)
50. feature "EGLN3" (0.003421)
51. feature "RPL8" (0.003396)
52. feature "MARCKS" (0.003125)
53. feature "S100A6" (0.003095)
54. feature "AHNAK2" (0.003091)
55. feature "RPLP2" (0.003005)
56. feature "RPS3" (0.002872)
57. feature "RPS8" (0.002848)
58. feature "MT-TQ" (0.002847)
59. feature "CBX3" (0.002841)
60. feature "PRRG3" (0.002833)
61. feature "RPL12" (0.002777)
62. feature "CALR" (0.002732)
63. feature "RPS12" (0.002717)
64. feature "HSPA5" (0.002670)
65. feature "RPS2" (0.002648)
66. feature "RPS19" (0.002635)
67. feature "RPL10" (0.002626)
68. feature "GSTP1" (0.002616)
69. feature "RPS15" (0.002610)
70. feature "H1-3" (0.002476)
71. feature "RPL27A" (0.002467)
72. feature "MT2A" (0.002429)
73. feature "EZR" (0.002410)
74. feature "YBX1" (0.002402)
75. feature "RPL28" (0.002399)
76. feature "HNRNPA2B1" (0.002394)
77. feature "HEPACAM" (0.002387)
78. feature "CAV1" (0.002366)
79. feature "PDIA3" (0.002365)
80. feature "TMSB4X" (0.002336)
81. feature "RPL36" (0.002330)
82. feature "H1-5" (0.002328)
83. feature "H2AC12" (0.002326)
84. feature "PTMS" (0.002316)
85. feature "BNIP3L" (0.002314)
86. feature "GAPDH" (0.002304)
87. feature "RPL13" (0.002242)
88. feature "RPL21" (0.002229)
89. feature "MT-TV" (0.002224)
90. feature "BAP1" (0.002203)
91. feature "DNMT1" (0.002179)
92. feature "TPT1" (0.002123)
93. feature "RPL35A" (0.002097)
94. feature "POLR2L" (0.002075)
95. feature "DSP" (0.002050)
96. feature "S100A2" (0.002043)
97. feature "RAC1" (0.002010)
98. feature "RPL18" (0.002003)
99. feature "FOSL1" (0.001976)
100. feature "C19orf53" (0.001948)
101. feature "TPI1" (0.001940)
102. feature "DEK" (0.001934)
103. feature "HNRNPM" (0.001907)
104. feature "ATP5MG" (0.001885)
105. feature "ATP5F1E" (0.001871)
106. feature "RPS14" (0.001855)
107. feature "KCNJ3" (0.001843)
108. feature "CENPF" (0.001808)
109. feature "RPSA" (0.001802)
110. feature "CNNM2" (0.001797)
111. feature "RPL37A" (0.001788)
112. feature "MKI67" (0.001786)
113. feature "H1-1" (0.001777)
114. feature "PKM" (0.001763)
115. feature "RPL37" (0.001758)
116. feature "STMN1" (0.001731)
117. feature "ELOB" (0.001722)
118. feature "ATAD2" (0.001684)
119. feature "TRIM44" (0.001673)
120. feature "GPI" (0.001665)
121. feature "H1-2" (0.001611)
122. feature "EEF1A1" (0.001609)
123. feature "HMGA1" (0.001601)
124. feature "MT-RNR1" (0.001596)
125. feature "MT1X" (0.001592)
126. feature "APLP2" (0.001586)
127. feature "PLEC" (0.001586)
128. feature "UQCRQ" (0.001552)
129. feature "TOP2A" (0.001551)
130. feature "NFE2L2" (0.001551)
131. feature "FOXP1" (0.001526)
132. feature "C4orf3" (0.001500)
133. feature "SFN" (0.001477)
134. feature "TPX2" (0.001454)
135. feature "PABPC1" (0.001439)
136. feature "UQCR11" (0.001401)
137. feature "RPS21" (0.001400)
138. feature "RPL18A" (0.001381)
139. feature "PLOD2" (0.001370)
140. feature "H1-4" (0.001358)
141. feature "H4C5" (0.001353)
142. feature "HSPB1" (0.001352)
143. feature "NDUFB2" (0.001342)
144. feature "KRT7" (0.001336)
145. feature "BRD4" (0.001333)
146. feature "ACTG1" (0.001326)
147. feature "NRBP1" (0.001309)
148. feature "KIF5B" (0.001302)
149. feature "PPDPF" (0.001300)
150. feature "ANXA1" (0.001290)
151. feature "KCNJ2" (0.001279)
152. feature "PDIA6" (0.001247)
153. feature "HDGF" (0.001237)
154. feature "RTF1" (0.001230)
155. feature "HMGB2" (0.001215)
156. feature "UBC" (0.001186)
157. feature "SLC2A1" (0.001176)
158. feature "KRT13" (0.001175)
159. feature "ANP32B" (0.001174)
160. feature "GDPGP1" (0.001170)
161. feature "FABP5" (0.001157)
162. feature "ZBTB20" (0.001156)
163. feature "LAMB3" (0.001139)
164. feature "GBP1P1" (0.001133)
165. feature "NMD3" (0.001128)
166. feature "COPS9" (0.001114)
167. feature "NEDD4L" (0.001111)
168. feature "MT-CO2" (0.001111)
169. feature "TSR1" (0.001099)
170. feature "LINC01304" (0.001085)
171. feature "PPP1R14B" (0.001076)
172. feature "GOLGA4" (0.001075)
173. feature "H3-3A" (0.001056)
174. feature "AHNAK" (0.001043)
175. feature "YBX3" (0.001043)
176. feature "PFN1" (0.001041)
177. feature "OAZ1" (0.001030)
178. feature "NDUFB10" (0.001024)
179. feature "ATP1A1" (0.001014)
180. feature "RPS29" (0.001014)
181. feature "CAVIN1" (0.001008)
182. feature "H3C2" (0.000985)
183. feature "ATP5ME" (0.000979)
184. feature "MARCHF6" (0.000970)
185. feature "HSBP1" (0.000968)
186. feature "KDM1A" (0.000957)
187. feature "KRT14" (0.000955)
188. feature "SLC25A48" (0.000949)
189. feature "MT-ND4L" (0.000943)
190. feature "MZT2B" (0.000922)
191. feature "KRT5" (0.000912)
192. feature "PPIG" (0.000910)
193. feature "BTN3A2" (0.000909)
194. feature "METTL26" (0.000886)
195. feature "H2AC11" (0.000879)
196. feature "CAMK2N1" (0.000877)
197. feature "MYH9" (0.000877)
198. feature "MT-CO3" (0.000848)
199. feature "UQCC2" (0.000840)
200. feature "GTF3A" (0.000838)
201. feature "MT-ND4" (0.000834)
202. feature "CAP1" (0.000831)
203. feature "LRRC59" (0.000826)
204. feature "ATOX1" (0.000817)
205. feature "TAF15" (0.000816)
206. feature "CCDC34" (0.000813)
207. feature "MYL12B" (0.000793)
208. feature "DDIT4" (0.000789)
209. feature "HES1" (0.000788)
210. feature "POLR2A" (0.000787)
211. feature "GNAS" (0.000784)
212. feature "ERO1A" (0.000783)
213. feature "RPL27" (0.000765)
214. feature "TMA7" (0.000762)
215. feature "MRPL33" (0.000760)
216. feature "CAVIN3" (0.000759)
217. feature "RPS6KA6" (0.000755)
218. feature "MT-ND6" (0.000735)
219. feature "GUK1" (0.000734)
220. feature "NCOR2" (0.000730)
221. feature "RPS26" (0.000728)
222. feature "SHOX" (0.000727)
223. feature "GPX4" (0.000722)
224. feature "PDLIM1" (0.000719)
225. feature "MLLT3" (0.000715)
226. feature "NCOR1" (0.000708)
227. feature "RAP2B" (0.000707)
228. feature "DAAM1" (0.000705)
229. feature "MRPL55" (0.000703)
230. feature "ANP32A" (0.000701)
231. feature "KRT10" (0.000700)
232. feature "CEP55" (0.000698)
233. feature "CAMSAP2" (0.000696)
234. feature "POLR2J" (0.000696)
235. feature "TCEAL4" (0.000691)
236. feature "ZHX1" (0.000690)
237. feature "YTHDC1" (0.000686)
238. feature "CLIP1" (0.000674)
239. feature "PAQR7" (0.000670)
240. feature "CSTB" (0.000660)
241. feature "RBX1" (0.000649)
242. feature "CDK2AP1" (0.000646)
243. feature "MT-CYB" (0.000645)
244. feature "CSTA" (0.000641)
245. feature "JUP" (0.000630)
246. feature "LUZP1" (0.000628)
247. feature "SYT14" (0.000628)
248. feature "SOX4" (0.000628)
249. feature "RANGAP1" (0.000627)
250. feature "MAFF" (0.000624)
251. feature "CNN3" (0.000623)
252. feature "NUDT1" (0.000618)
253. feature "MZT2A" (0.000617)
254. feature "FLNA" (0.000614)
255. feature "HSPA4" (0.000613)
256. feature "MPHOSPH6" (0.000611)
257. feature "APOOL" (0.000610)
258. feature "EPPK1" (0.000610)
259. feature "JUN" (0.000607)
260. feature "H2AZ2" (0.000603)
261. feature "MT-ND5" (0.000594)
262. feature "HCFC1R1" (0.000594)
263. feature "H2BC4" (0.000591)
264. feature "MAP3K13" (0.000584)
265. feature "ATP2A2" (0.000582)
266. feature "CD47" (0.000576)
267. feature "HCFC1" (0.000574)
268. feature "SIVA1" (0.000563)
269. feature "BOD1L1" (0.000559)
270. feature "TAOK3" (0.000558)
271. feature "DDX54" (0.000556)
272. feature "FOSL2" (0.000553)
273. feature "MT-ND1" (0.000552)
274. feature "MTA2" (0.000551)
275. feature "PAIP2" (0.000550)
276. feature "SMARCC1" (0.000548)
277. feature "LIMCH1" (0.000547)
278. feature "HMGN3" (0.000546)
279. feature "TSPO" (0.000540)
280. feature "RPL7A" (0.000538)
281. feature "SENP7" (0.000535)
282. feature "SNRNP70" (0.000532)
283. feature "FAM107B" (0.000530)
284. feature "NMT2" (0.000528)
285. feature "LMNB2" (0.000527)
286. feature "PRRC2A" (0.000527)
287. feature "LARP7" (0.000526)
288. feature "TMEM160" (0.000524)
289. feature "LBH" (0.000523)
290. feature "PRMT2" (0.000523)
291. feature "CTNND1" (0.000522)
292. feature "PDCD4" (0.000520)
293. feature "H2BC9" (0.000520)
294. feature "OPTN" (0.000518)
295. feature "PLBD2" (0.000518)
296. feature "COTL1" (0.000518)
297. feature "PRR11" (0.000512)
298. feature "KIF3B" (0.000508)
299. feature "MCM4" (0.000502)
300. feature "SOS1" (0.000501)
301. feature "ARID5B" (0.000498)
302. feature "CAPN1" (0.000494)
303. feature "BRMS1" (0.000494)
304. feature "MT1E" (0.000493)
305. feature "FAM83A" (0.000491)
306. feature "HSPBP1" (0.000491)
307. feature "ZCRB1" (0.000488)
308. feature "CARMIL1" (0.000483)
309. feature "TAGLN2" (0.000480)
310. feature "SIGMAR1" (0.000479)
311. feature "FAM162A" (0.000477)
312. feature "MT-ND2" (0.000476)
313. feature "PDXK" (0.000473)
314. feature "HK1" (0.000470)
315. feature "CDK2AP2" (0.000470)
316. feature "MT-CO1" (0.000468)
317. feature "CCNI" (0.000464)
318. feature "TCEAL9" (0.000462)
319. feature "OIP5-AS1" (0.000461)
320. feature "DRAP1" (0.000457)
321. feature "RBM23" (0.000455)
322. feature "YKT6" (0.000453)
323. feature "H2AC16" (0.000448)
324. feature "CFAP251" (0.000448)
325. feature "CTBP2" (0.000445)
326. feature "RNF20" (0.000445)
327. feature "MT-ATP6" (0.000444)
328. feature "CLIC1" (0.000441)
329. feature "AMOTL1" (0.000441)
330. feature "C4orf48" (0.000441)
331. feature "FEM1A" (0.000440)
332. feature "PHGDH" (0.000440)
333. feature "MAP7" (0.000440)
334. feature "ARF3" (0.000440)
335. feature "PFKP" (0.000439)
336. feature "GNB1" (0.000439)
337. feature "SAMD4A" (0.000439)
338. feature "PFKFB3" (0.000437)
339. feature "MRPL14" (0.000436)
340. feature "LEO1" (0.000436)
341. feature "ZNF148" (0.000435)
342. feature "ARHGDIA" (0.000433)
343. feature "ITGA3" (0.000431)
344. feature "SLC25A37" (0.000431)
345. feature "TOP2B" (0.000430)
346. feature "MT-ATP8" (0.000430)
347. feature "CDKN3" (0.000429)
348. feature "DCTN1" (0.000429)
349. feature "CAPZB" (0.000428)
350. feature "SETD2" (0.000427)
351. feature "ANAPC16" (0.000425)
352. feature "METRN" (0.000425)
353. feature "C12orf75" (0.000423)
354. feature "PUS7" (0.000421)
355. feature "CFAP36" (0.000420)
356. feature "AK4" (0.000419)
357. feature "BRI3" (0.000419)
358. feature "AKT1S1" (0.000418)
359. feature "MXI1" (0.000415)
360. feature "PGAM1" (0.000414)
361. feature "SMARCB1" (0.000414)
362. feature "TIMP3" (0.000414)
363. feature "RRAS" (0.000413)
364. feature "RAB13" (0.000413)
365. feature "MTND1P23" (0.000412)
366. feature "POLE4" (0.000412)
367. feature "UPF3B" (0.000411)
368. feature "S100A13" (0.000411)
369. feature "FUBP3" (0.000410)
370. feature "RBFOX2" (0.000409)
371. feature "PRDM2" (0.000409)
372. feature "RFC1" (0.000406)
373. feature "CDK5RAP2" (0.000405)
374. feature "PTGR1" (0.000405)
375. feature "KRT17" (0.000405)
376. feature "SNHG9" (0.000405)
377. feature "ZCCHC9" (0.000404)
378. feature "LRRFIP2" (0.000404)
379. feature "LGALS1" (0.000404)
380. feature "IRAK1" (0.000403)
381. feature "KMT2D" (0.000402)
382. feature "ATN1" (0.000399)
383. feature "TK1" (0.000395)
384. feature "RRS1" (0.000394)
385. feature "FAM120A" (0.000394)
386. feature "RB1CC1" (0.000393)
387. feature "GOLGA3" (0.000393)
388. feature "KAT7" (0.000392)
389. feature "NUP62" (0.000392)
390. feature "FGFR1OP2" (0.000389)
391. feature "PAQR8" (0.000388)
392. feature "STC2" (0.000388)
393. feature "SNRPA" (0.000385)
394. feature "SEPTIN9" (0.000384)
395. feature "KLF6" (0.000382)
396. feature "SLC6A8" (0.000380)
397. feature "GIPC1" (0.000377)
398. feature "BRCC3" (0.000375)
399. feature "SMIM10L1" (0.000375)
400. feature "IGFL2-AS1" (0.000373)
401. feature "CCDC50" (0.000371)
402. feature "C12orf57" (0.000369)
403. feature "REST" (0.000368)
404. feature "CMAS" (0.000368)
405. feature "PSIP1" (0.000365)
406. feature "RHOA" (0.000364)
407. feature "DNMT3A" (0.000364)
408. feature "PSAP" (0.000360)
409. feature "TAF13" (0.000359)
410. feature "ID1" (0.000359)
411. feature "KIAA1522" (0.000359)
412. feature "NDUFB1" (0.000359)
413. feature "LINC01133" (0.000358)
414. feature "ODF2" (0.000355)
415. feature "MT-ND3" (0.000355)
416. feature "COX14" (0.000354)
417. feature "RGPD4-AS1" (0.000352)
418. feature "PPP4R2" (0.000352)
419. feature "SEM1" (0.000351)
420. feature "TOLLIP" (0.000351)
421. feature "COX8A" (0.000350)
422. feature "VIT" (0.000348)
423. feature "ING2" (0.000347)
424. feature "IFI27L2" (0.000346)
425. feature "ROCK2" (0.000346)
426. feature "TMEM141" (0.000345)
427. feature "HNRNPA1" (0.000345)
428. feature "TMEM238" (0.000344)
429. feature "CDA" (0.000344)
430. feature "CMIP" (0.000343)
431. feature "PHF3" (0.000342)
432. feature "TNIP1" (0.000342)
433. feature "MRPS33" (0.000340)
434. feature "DLD" (0.000340)
435. feature "MSANTD3" (0.000340)
436. feature "DDX23" (0.000339)
437. feature "ZFP36L1" (0.000337)
438. feature "ARID1A" (0.000336)
439. feature "FLOT1" (0.000336)
440. feature "DTYMK" (0.000335)
441. feature "ZNF316" (0.000334)
442. feature "SART1" (0.000331)
443. feature "NIBAN2" (0.000331)
444. feature "EHD2" (0.000329)
445. feature "MIR663AHG" (0.000329)
446. feature "ZMYND8" (0.000327)
447. feature "CALB1" (0.000327)
448. feature "CCDC167" (0.000327)
449. feature "PCBD1" (0.000326)
450. feature "RAB4A" (0.000324)
451. feature "LAGE3" (0.000323)
452. feature "GOLGA5" (0.000322)
453. feature "THRB" (0.000322)
454. feature "NIN" (0.000321)
455. feature "MT-TS1" (0.000318)
456. feature "LINC02367" (0.000317)
457. feature "RAB11B" (0.000317)
458. feature "TF" (0.000316)
459. feature "ISOC2" (0.000316)
460. feature "KMT5B" (0.000314)
461. feature "JOSD2" (0.000312)
462. feature "PRR34-AS1" (0.000312)
463. feature "CD151" (0.000311)
464. feature "WBP4" (0.000311)
465. feature "KEAP1" (0.000310)
466. feature "DCTN2" (0.000309)
467. feature "PPP2R1A" (0.000309)
468. feature "AXL" (0.000308)
469. feature "CNOT6L" (0.000308)
470. feature "SGO2" (0.000305)
471. feature "SERINC2" (0.000305)
472. feature "COMMD6" (0.000305)
473. feature "SEC61G" (0.000305)
474. feature "WIPF2" (0.000303)
475. feature "UBE2Q2" (0.000303)
476. feature "EMP2" (0.000302)
477. feature "LSM14A" (0.000300)
478. feature "EFNA2" (0.000299)
479. feature "IPO9" (0.000299)
480. feature "THOC7" (0.000299)
481. feature "SLC4A1" (0.000297)
482. feature "FOXRED2" (0.000297)
483. feature "CCDC88A" (0.000297)
484. feature "PROSER1" (0.000295)
485. feature "MACC1" (0.000295)
486. feature "GATA3" (0.000294)
487. feature "TCF20" (0.000293)
488. feature "VRK3" (0.000293)
489. feature "ZBTB38" (0.000293)
490. feature "SCRIB" (0.000292)
491. feature "RGS10" (0.000291)
492. feature "CDC42BPA" (0.000291)
493. feature "CENPB" (0.000291)
494. feature "SPRR1B" (0.000290)
495. feature "MORN2" (0.000290)
496. feature "PALLD" (0.000290)
497. feature "FAM20B" (0.000289)
498. feature "RHOD" (0.000289)
499. feature "PRR14L" (0.000288)
500. feature "MDC1" (0.000288)
501. feature "CIRBP" (0.000287)
502. feature "EPAS1" (0.000287)
503. feature "DIO2" (0.000287)
504. feature "DCAF7" (0.000286)
505. feature "MIR4458HG" (0.000286)
506. feature "PPL" (0.000285)
507. feature "STRN4" (0.000285)
508. feature "LSM4" (0.000285)
509. feature "NAB1" (0.000284)
510. feature "NDUFA2" (0.000284)
511. feature "H19" (0.000283)
512. feature "SMC6" (0.000283)
513. feature "HOXB7" (0.000283)
514. feature "ARL13B" (0.000282)
515. feature "AGRN" (0.000282)
516. feature "DNM2" (0.000281)
517. feature "SYNJ2" (0.000280)
518. feature "CCDC57" (0.000279)
519. feature "FBXW5" (0.000278)
520. feature "ADAM15" (0.000278)
521. feature "TNS4" (0.000278)
522. feature "RIN2" (0.000277)
523. feature "AP1M1" (0.000277)
524. feature "ZFHX3" (0.000276)
525. feature "STT3A" (0.000275)
526. feature "ZNF268" (0.000274)
527. feature "MAP3K4" (0.000273)
528. feature "UBE2C" (0.000273)
529. feature "PKP4" (0.000272)
530. feature "AKR1A1" (0.000272)
531. feature "EGR1" (0.000272)
532. feature "HMGN5" (0.000270)
533. feature "MAPK14" (0.000270)
534. feature "SLPI" (0.000268)
535. feature "MT-TY" (0.000268)
536. feature "EPB41L1" (0.000268)
537. feature "PCBP1" (0.000267)
538. feature "PTOV1" (0.000266)
539. feature "UCA1" (0.000266)
540. feature "SLC2A4RG" (0.000266)
541. feature "NEUROD1" (0.000265)
542. feature "MAST4" (0.000265)
543. feature "CANT1" (0.000265)
544. feature "TPD52L1" (0.000264)
545. feature "SMDT1" (0.000263)
546. feature "TWNK" (0.000262)
547. feature "EPB41L2" (0.000262)
548. feature "TMEM52B" (0.000260)
549. feature "ARHGEF7" (0.000260)
550. feature "PHLDB2" (0.000260)
551. feature "KCNJ15" (0.000260)
552. feature "TTC19" (0.000259)
553. feature "LETM1" (0.000259)
554. feature "RNF10" (0.000258)
555. feature "DYNC2I2" (0.000258)
556. feature "NEUROD2" (0.000258)
557. feature "HRAS" (0.000256)
558. feature "SAMD4B" (0.000254)
559. feature "ITSN1" (0.000254)
560. feature "ZNF688" (0.000253)
561. feature "ZNF146" (0.000251)
562. feature "ZBTB45" (0.000251)
563. feature "MYH14" (0.000250)
564. feature "SLC25A1" (0.000249)
565. feature "CREB1" (0.000249)
566. feature "NUDT14" (0.000248)
567. feature "MED29" (0.000245)
568. feature "GNL1" (0.000245)
569. feature "MKNK1" (0.000244)
570. feature "RAB35" (0.000244)
571. feature "PLAAT3" (0.000244)
572. feature "BRK1" (0.000244)
573. feature "MGMT" (0.000243)
574. feature "AKR7A2" (0.000243)
575. feature "SCNM1" (0.000242)
576. feature "LAMTOR2" (0.000242)
577. feature "UBE2I" (0.000242)
578. feature "YTHDF3" (0.000242)
579. feature "MNS1" (0.000241)
580. feature "CPEB1" (0.000241)
581. feature "MKNK2" (0.000241)
582. feature "IRF2BPL" (0.000240)
583. feature "SCP2" (0.000240)
584. feature "RPSAP48" (0.000240)
585. feature "CDC42EP1" (0.000239)
586. feature "TUBA1C" (0.000237)
587. feature "RRM1" (0.000237)
588. feature "POLR3A" (0.000237)
589. feature "SNHG6" (0.000236)
590. feature "ANGPTL4" (0.000236)
591. feature "NARF" (0.000236)
592. feature "FAM114A1" (0.000235)
593. feature "FRY" (0.000234)
594. feature "DDIT3" (0.000233)
595. feature "UACA" (0.000233)
596. feature "DPCD" (0.000233)
597. feature "MAP2" (0.000233)
598. feature "HS6ST1" (0.000232)
599. feature "TNNT1" (0.000232)
600. feature "RASGEF1B" (0.000232)
601. feature "ORC4" (0.000231)
602. feature "PLCXD2" (0.000231)
603. feature "RP9" (0.000231)
604. feature "MRFAP1" (0.000231)
605. feature "CLDN11" (0.000230)
606. feature "ARHGEF26" (0.000230)
607. feature "CAV2" (0.000230)
608. feature "USP53" (0.000229)
609. feature "PSMA5" (0.000229)
610. feature "ZNF462" (0.000229)
611. feature "TTL" (0.000229)
612. feature "UIMC1" (0.000229)
613. feature "MLXIP" (0.000228)
614. feature "TSR3" (0.000228)
615. feature "YTHDF1" (0.000227)
616. feature "NEIL3" (0.000227)
617. feature "NUMA1" (0.000227)
618. feature "LIMS1" (0.000227)
619. feature "ARFGEF3" (0.000227)
620. feature "LYPD4" (0.000227)
621. feature "NDUFC1" (0.000226)
622. feature "HMGB1P20" (0.000225)
623. feature "PRPF31" (0.000225)
624. feature "RAB3GAP1" (0.000225)
625. feature "ITSN2" (0.000225)
626. feature "CYB5R3" (0.000224)
627. feature "NDUFB4" (0.000224)
628. feature "ARL2" (0.000224)
629. feature "ZFC3H1" (0.000224)
630. feature "JUND" (0.000223)
631. feature "EXOC7" (0.000223)
632. feature "MRPS6" (0.000223)
633. feature "SH3BGRL" (0.000223)
634. feature "CHTOP" (0.000223)
635. feature "MYO1C" (0.000222)
636. feature "ZNF609" (0.000221)
637. feature "SKI" (0.000219)
638. feature "EIF5AL1" (0.000218)
639. feature "MBD2" (0.000218)
640. feature "NCAM1" (0.000218)
641. feature "FAM126B" (0.000217)
642. feature "SLC25A23" (0.000217)
643. feature "MCM5" (0.000216)
644. feature "INHBA" (0.000215)
645. feature "RAD54L2" (0.000215)
646. feature "TCF3" (0.000215)
647. feature "H2AX" (0.000215)
648. feature "CEP83" (0.000215)
649. feature "CEP250" (0.000215)
650. feature "C1orf21" (0.000215)
651. feature "NCLN" (0.000215)
652. feature "RELA" (0.000214)
653. feature "TAF10" (0.000214)
654. feature "DGKD" (0.000214)
655. feature "STX2" (0.000214)
656. feature "SLMAP" (0.000214)
657. feature "PPARA" (0.000213)
658. feature "MAP2K3" (0.000213)
659. feature "GTF3C5" (0.000213)
660. feature "SLC1A5" (0.000212)
661. feature "ZC3H4" (0.000212)
662. feature "CBX4" (0.000212)
663. feature "GADD45A" (0.000212)
664. feature "BMPR1B" (0.000212)
665. feature "RAB12" (0.000211)
666. feature "ST14" (0.000211)
667. feature "DHX40" (0.000210)
668. feature "EID1" (0.000210)
669. feature "ENTR1" (0.000210)
670. feature "PUSL1" (0.000210)
671. feature "LINC02511" (0.000210)
672. feature "TNFRSF12A" (0.000210)
673. feature "FBRSL1" (0.000210)
674. feature "GNAI2" (0.000209)
675. feature "GNG5" (0.000209)
676. feature "PGLS" (0.000209)
677. feature "OSBPL3" (0.000209)
678. feature "MOB3A" (0.000209)
679. feature "DUSP23" (0.000209)
680. feature "CHCHD7" (0.000208)
681. feature "CAPNS1" (0.000207)
682. feature "GRK2" (0.000207)
683. feature "PLCE1" (0.000206)
684. feature "LINC02762" (0.000206)
685. feature "DNAAF5" (0.000206)
686. feature "PIAS2" (0.000206)
687. feature "MCM3AP" (0.000205)
688. feature "MINK1" (0.000205)
689. feature "NEK2" (0.000204)
690. feature "RNF122" (0.000204)
691. feature "SLC25A5" (0.000204)
692. feature "NACA4P" (0.000204)
693. feature "SPINT1-AS1" (0.000203)
694. feature "ZNF318" (0.000202)
695. feature "DPM3" (0.000202)
696. feature "CTSZ" (0.000202)
697. feature "SNAPC1" (0.000201)
698. feature "RIPK1" (0.000201)
699. feature "CREB3L2" (0.000201)
700. feature "INO80E" (0.000200)
701. feature "ZFAS1" (0.000200)
702. feature "POLR2F" (0.000200)
703. feature "CIAO2A" (0.000200)
704. feature "SELENOH" (0.000199)
705. feature "EHBP1L1" (0.000199)
706. feature "MXRA5" (0.000199)
707. feature "ESCO1" (0.000199)
708. feature "UBP1" (0.000198)
709. feature "FOXO3" (0.000198)
710. feature "PLK1" (0.000198)
711. feature "WDR54" (0.000198)
712. feature "PMEPA1" (0.000198)
713. feature "FAM76B" (0.000198)
714. feature "METTL5" (0.000197)
715. feature "CETN3" (0.000197)
716. feature "TCEAL3" (0.000197)
717. feature "TBC1D20" (0.000197)
718. feature "DGUOK" (0.000197)
719. feature "ERRFI1" (0.000197)
720. feature "BICD1" (0.000197)
721. feature "VMA21" (0.000194)
722. feature "HIF3A" (0.000194)
723. feature "CASK" (0.000193)
724. feature "NDUFA8" (0.000193)
725. feature "PPP1R21" (0.000193)
726. feature "SCAF8" (0.000193)
727. feature "SPG21" (0.000193)
728. feature "GSS" (0.000192)
729. feature "PAPSS1" (0.000192)
730. feature "CLDN4" (0.000192)
731. feature "ITPR3" (0.000192)
732. feature "TEX261" (0.000191)
733. feature "NEDD1" (0.000191)
734. feature "DYNLT3" (0.000191)
735. feature "MORC3" (0.000190)
736. feature "TEDC2-AS1" (0.000189)
737. feature "GADD45B" (0.000189)
738. feature "BIK" (0.000189)
739. feature "PIN4" (0.000189)
740. feature "CHPF" (0.000188)
741. feature "C5orf38" (0.000187)
742. feature "FOXP4" (0.000187)
743. feature "MDFI" (0.000186)
744. feature "L3MBTL2" (0.000186)
745. feature "LINC01234" (0.000185)
746. feature "CLIC4" (0.000185)
747. feature "FGD4" (0.000185)
748. feature "TCEAL1" (0.000185)
749. feature "LINC02582" (0.000185)
750. feature "NAGK" (0.000184)
751. feature "LEMD3" (0.000184)
752. feature "BACH1" (0.000184)
753. feature "MYO5C" (0.000183)
754. feature "SRI" (0.000183)
755. feature "ZDHHC12" (0.000183)
756. feature "PLSCR1" (0.000183)
757. feature "CUL4B" (0.000183)
758. feature "MTUS1" (0.000182)
759. feature "IER3IP1" (0.000182)
760. feature "SNAPC3" (0.000182)
761. feature "RBSN" (0.000182)
762. feature "APPL1" (0.000180)
763. feature "CBWD3" (0.000180)
764. feature "ZNF511" (0.000180)
765. feature "SECISBP2" (0.000180)
766. feature "CDT1" (0.000180)
767. feature "PTPRE" (0.000180)
768. feature "SRA1" (0.000180)
769. feature "RBAK" (0.000180)
770. feature "FAM200B" (0.000179)
771. feature "B3GAT2" (0.000179)
772. feature "MAB21L3" (0.000179)
773. feature "MBD6" (0.000179)
774. feature "FCHO2" (0.000179)
775. feature "RNF4" (0.000178)
776. feature "MRPS2" (0.000178)
777. feature "IRS1" (0.000178)
778. feature "TNKS1BP1" (0.000178)
779. feature "CBX6" (0.000178)
780. feature "RAB31" (0.000178)
781. feature "SLITRK5" (0.000178)
782. feature "INSR" (0.000177)
783. feature "PHRF1" (0.000177)
784. feature "BNC1" (0.000177)
785. feature "ZBED4" (0.000177)
786. feature "MSR1" (0.000176)
787. feature "TMSB4XP4" (0.000175)
788. feature "HIVEP3" (0.000175)
789. feature "HMGCS1" (0.000175)
790. feature "PMPCA" (0.000174)
791. feature "AVPI1" (0.000174)
792. feature "EIF3F" (0.000174)
793. feature "NR2F6" (0.000174)
794. feature "PPIH" (0.000174)
795. feature "ANKRD40" (0.000173)
796. feature "HERC1" (0.000173)
797. feature "ERF" (0.000173)
798. feature "SHKBP1" (0.000173)
799. feature "ATXN2L" (0.000173)
800. feature "MPDU1" (0.000173)
801. feature "ESRP2" (0.000173)
802. feature "LINC01503" (0.000172)
803. feature "WDR82" (0.000171)
804. feature "MAGEA4" (0.000171)
805. feature "RPGRIP1L" (0.000171)
806. feature "VEGFB" (0.000171)
807. feature "QSOX1" (0.000170)
808. feature "ZNF324" (0.000170)
809. feature "LINC02345" (0.000170)
810. feature "MED25" (0.000169)
811. feature "MAD2L2" (0.000169)
812. feature "HES4" (0.000169)
813. feature "GFOD1" (0.000169)
814. feature "RAB5A" (0.000169)
815. feature "TCAF1" (0.000168)
816. feature "ZNF385A" (0.000167)
817. feature "GDE1" (0.000167)
818. feature "TRMT5" (0.000167)
819. feature "EPS8L2" (0.000167)
820. feature "TUBB6" (0.000167)
821. feature "AMFR" (0.000167)
822. feature "ZMIZ1" (0.000166)
823. feature "KIF16B" (0.000166)
824. feature "TNFSF13B" (0.000166)
825. feature "EEF2K" (0.000166)
826. feature "MAD2L1" (0.000165)
827. feature "AGBL5" (0.000165)
828. feature "AP2A1" (0.000165)
829. feature "WDR18" (0.000165)
830. feature "ANXA3" (0.000164)
831. feature "GDI1" (0.000164)
832. feature "COL7A1" (0.000163)
833. feature "OBSCN" (0.000163)
834. feature "PYCARD" (0.000163)
835. feature "MINDY2" (0.000163)
836. feature "RPL12P38" (0.000162)
837. feature "UBE2D1" (0.000162)
838. feature "ETAA1" (0.000162)
839. feature "CARM1" (0.000162)
840. feature "LDOC1" (0.000162)
841. feature "TOMM5" (0.000161)
842. feature "NCK1" (0.000161)
843. feature "PAPSS2" (0.000160)
844. feature "RARG" (0.000160)
845. feature "ANKMY2" (0.000160)
846. feature "BRWD3" (0.000159)
847. feature "NEDD9" (0.000159)
848. feature "TASOR" (0.000159)
849. feature "TINAGL1" (0.000158)
850. feature "NUP93" (0.000158)
851. feature "MCM6" (0.000157)
852. feature "SF3B4" (0.000157)
853. feature "UBN2" (0.000157)
854. feature "EFNB2" (0.000157)
855. feature "SPRYD4" (0.000157)
856. feature "PYCR1" (0.000156)
857. feature "KDM4B" (0.000156)
858. feature "MARVELD1" (0.000155)
859. feature "DGUOK-AS1" (0.000155)
860. feature "MLLT10" (0.000155)
861. feature "CNTRL" (0.000155)
862. feature "SPRYD3" (0.000155)
863. feature "SOX11" (0.000155)
864. feature "SCCPDH" (0.000154)
865. feature "EFCAB2" (0.000154)
866. feature "RAB5B" (0.000153)
867. feature "TTYH3" (0.000152)
868. feature "ARID4A" (0.000152)
869. feature "FKBP9" (0.000152)
870. feature "PFKL" (0.000152)
871. feature "IQCG" (0.000152)
872. feature "YEATS4" (0.000151)
873. feature "MLLT6" (0.000151)
874. feature "FLAD1" (0.000151)
875. feature "UNC45A" (0.000151)
876. feature "MT-TE" (0.000151)
877. feature "FUOM" (0.000150)
878. feature "POLDIP2" (0.000150)
879. feature "BTBD2" (0.000150)
880. feature "SLC38A10" (0.000150)
881. feature "TCF19" (0.000150)
882. feature "SDC1" (0.000149)
883. feature "ARHGAP23" (0.000149)
884. feature "CRBN" (0.000149)
885. feature "DANT1" (0.000149)
886. feature "SETBP1" (0.000149)
887. feature "MMP2" (0.000149)
888. feature "DGKZ" (0.000148)
889. feature "ADORA2B" (0.000148)
890. feature "SDHB" (0.000148)
891. feature "ACSL4" (0.000148)
892. feature "REXO1" (0.000148)
893. feature "FAM181B" (0.000148)
894. feature "IER5" (0.000148)
895. feature "ARID3A" (0.000148)
896. feature "SPA17" (0.000148)
897. feature "PSMG1" (0.000147)
898. feature "KXD1" (0.000147)
899. feature "RUSC1" (0.000147)
900. feature "IMP3" (0.000147)
901. feature "NSMF" (0.000147)
902. feature "ECH1" (0.000147)
903. feature "UBQLN4" (0.000147)
904. feature "GATD1" (0.000147)
905. feature "GALM" (0.000147)
906. feature "KLHL7" (0.000147)
907. feature "ZNF598" (0.000146)
908. feature "FASTKD3" (0.000146)
909. feature "LPAR4" (0.000146)
910. feature "LINC-PINT" (0.000146)
911. feature "TAF12" (0.000146)
912. feature "ASNSD1" (0.000146)
913. feature "ARAF" (0.000146)
914. feature "CAPG" (0.000145)
915. feature "SIRT1" (0.000145)
916. feature "HHLA3" (0.000145)
917. feature "TMEM256" (0.000145)
918. feature "SFMBT1" (0.000145)
919. feature "PCDHGA10" (0.000145)
920. feature "KRT4" (0.000145)
921. feature "SAMHD1" (0.000144)
922. feature "NRAV" (0.000144)
923. feature "SFXN3" (0.000144)
924. feature "ATF3" (0.000144)
925. feature "VAMP7" (0.000144)
926. feature "SINHCAFP3" (0.000143)
927. feature "SLC25A46" (0.000143)
928. feature "CSRP1" (0.000143)
929. feature "P2RY2" (0.000143)
930. feature "MBD3" (0.000143)
931. feature "MRPL49" (0.000143)
932. feature "IL18" (0.000143)
933. feature "COMT" (0.000143)
934. feature "TNFAIP8L1" (0.000142)
935. feature "SSNA1" (0.000142)
936. feature "ACVR2B" (0.000141)
937. feature "ZNF580" (0.000141)
938. feature "AJAP1" (0.000141)
939. feature "CCDC8" (0.000141)
940. feature "SMAD3" (0.000140)
941. feature "IRS2" (0.000140)
942. feature "KDM3A" (0.000140)
943. feature "IFT74" (0.000140)
944. feature "UNC13D" (0.000140)
945. feature "CPTP" (0.000140)
946. feature "NOP10" (0.000140)
947. feature "ARAP1" (0.000140)
948. feature "RPL17" (0.000140)
949. feature "PGM1" (0.000140)
950. feature "MAGED2" (0.000140)
951. feature "MRPL45" (0.000139)
952. feature "PLCD3" (0.000139)
953. feature "CCNG2" (0.000139)
954. feature "CTBP1" (0.000138)
955. feature "DCAF6" (0.000138)
956. feature "HMGB1P8" (0.000138)
957. feature "LAMA5" (0.000138)
958. feature "MARK1" (0.000138)
959. feature "RASSF3" (0.000137)
960. feature "TRDN" (0.000137)
961. feature "GYS1" (0.000137)
962. feature "AAGAB" (0.000137)
963. feature "PRPSAP2" (0.000137)
964. feature "UBE2E1" (0.000136)
965. feature "NEK1" (0.000136)
966. feature "LMF2" (0.000136)
967. feature "TRAFD1" (0.000136)
968. feature "FGF12" (0.000136)
969. feature "RAB2B" (0.000136)
970. feature "SHISA5" (0.000136)
971. feature "GNPAT" (0.000135)
972. feature "SOX15" (0.000135)
973. feature "PPP1R13L" (0.000135)
974. feature "DNA2" (0.000135)
975. feature "UBA3" (0.000135)
976. feature "MT-TC" (0.000135)
977. feature "BRAT1" (0.000134)
978. feature "DNPH1" (0.000134)
979. feature "KRT16" (0.000134)
980. feature "ZNF8-DT" (0.000134)
981. feature "RPLP0P2" (0.000133)
982. feature "CKB" (0.000133)
983. feature "SCPEP1" (0.000133)
984. feature "CEP63" (0.000133)
985. feature "ADM" (0.000133)
986. feature "MLH3" (0.000133)
987. feature "DLG5" (0.000132)
988. feature "TRIM7" (0.000132)
989. feature "TRIOBP" (0.000132)
990. feature "DBT" (0.000132)
991. feature "BLCAP" (0.000131)
992. feature "GMPPA" (0.000131)
993. feature "DYNC2I1" (0.000131)
994. feature "RNF187" (0.000130)
995. feature "KIRREL1" (0.000130)
996. feature "NUAK1" (0.000130)
997. feature "DKK1" (0.000130)
998. feature "VAV2" (0.000130)
999. feature "CNTROB" (0.000130)
1000. feature "TRMT1L" (0.000130)
1001. feature "COL6A3" (0.000129)
1002. feature "AXIN1" (0.000129)
1003. feature "TUBA5P" (0.000129)
1004. feature "HSPA2" (0.000129)
1005. feature "TLE3" (0.000129)
1006. feature "PLD5" (0.000129)
1007. feature "SGSM2" (0.000129)
1008. feature "CKS1B" (0.000129)
1009. feature "OXLD1" (0.000128)
1010. feature "ZNRF1" (0.000128)
1011. feature "UTP3" (0.000128)
1012. feature "COA5" (0.000128)
1013. feature "STK11" (0.000128)
1014. feature "AGAP1" (0.000128)
1015. feature "CNR2" (0.000127)
1016. feature "VSIR" (0.000127)
1017. feature "EFNA5" (0.000127)
1018. feature "TFPI2" (0.000126)
1019. feature "ZNF358" (0.000126)
1020. feature "TCF7L1" (0.000126)
1021. feature "PLEKHG3" (0.000126)
1022. feature "TONSL" (0.000126)
1023. feature "GPT2" (0.000126)
1024. feature "SNAP47" (0.000125)
1025. feature "ZNF280B" (0.000125)
1026. feature "SYK" (0.000125)
1027. feature "ZNF703" (0.000125)
1028. feature "CDK19" (0.000125)
1029. feature "IPO13" (0.000125)
1030. feature "CENPM" (0.000125)
1031. feature "MON1B" (0.000124)
1032. feature "TMEM40" (0.000124)
1033. feature "RHBDD2" (0.000124)
1034. feature "DTD1" (0.000124)
1035. feature "SORBS3" (0.000123)
1036. feature "HNRNPUL2" (0.000122)
1037. feature "S1PR2" (0.000122)
1038. feature "TTC28" (0.000122)
1039. feature "PPTC7" (0.000121)
1040. feature "NARS2" (0.000121)
1041. feature "CRYZ" (0.000121)
1042. feature "MOV10" (0.000121)
1043. feature "HILPDA" (0.000121)
1044. feature "FAM210B" (0.000121)
1045. feature "IVL" (0.000121)
1046. feature "LDB1" (0.000121)
1047. feature "CDC42BPG" (0.000121)
1048. feature "RNASET2" (0.000120)
1049. feature "MT-TS2" (0.000120)
1050. feature "ZFYVE9" (0.000120)
1051. feature "PPP2R5D" (0.000120)
1052. feature "OAZ2" (0.000119)
1053. feature "R3HCC1L" (0.000119)
1054. feature "OSTF1" (0.000119)
1055. feature "ERI1" (0.000119)
1056. feature "ULK1" (0.000119)
1057. feature "NELFB" (0.000118)
1058. feature "SCAPER" (0.000118)
1059. feature "ANKRD28" (0.000118)
1060. feature "BASP1" (0.000118)
1061. feature "KLLN" (0.000118)
1062. feature "SAT2" (0.000117)
1063. feature "CCS" (0.000117)
1064. feature "SIPA1L1" (0.000117)
1065. feature "C2orf68" (0.000117)
1066. feature "NUMBL" (0.000117)
1067. feature "PDE8A" (0.000117)
1068. feature "THEM4" (0.000117)
1069. feature "CITED2" (0.000117)
1070. feature "ZADH2" (0.000117)
1071. feature "SLC39A14" (0.000116)
1072. feature "CUEDC2" (0.000116)
1073. feature "NECAB3" (0.000116)
1074. feature "TMEM126A" (0.000116)
1075. feature "ZDHHC8" (0.000116)
1076. feature "VPS26B" (0.000116)
1077. feature "PMVK" (0.000115)
1078. feature "OGFRL1" (0.000115)
1079. feature "SLC35C2" (0.000115)
1080. feature "NCK1-DT" (0.000115)
1081. feature "RNF223" (0.000115)
1082. feature "KLC2" (0.000115)
1083. feature "WDR77" (0.000115)
1084. feature "EFNB1" (0.000115)
1085. feature "DTX4" (0.000115)
1086. feature "ATP13A2" (0.000115)
1087. feature "IFI6" (0.000115)
1088. feature "PRR5L" (0.000115)
1089. feature "ZNF317" (0.000115)
1090. feature "APEH" (0.000115)
1091. feature "ZBTB33" (0.000114)
1092. feature "SMIM30" (0.000114)
1093. feature "WBP2" (0.000114)
1094. feature "ZRANB1" (0.000114)
1095. feature "OVCH1-AS1" (0.000114)
1096. feature "GMPR2" (0.000114)
1097. feature "AK1" (0.000114)
1098. feature "TMEM70" (0.000114)
1099. feature "FHL2" (0.000113)
1100. feature "MID1IP1" (0.000113)
1101. feature "SH3RF1" (0.000113)
1102. feature "PLEKHA4" (0.000113)
1103. feature "RHOT2" (0.000113)
1104. feature "DAPK3" (0.000113)
1105. feature "FAM193A" (0.000113)
1106. feature "RAD51" (0.000112)
1107. feature "MAP3K14" (0.000112)
1108. feature "CEP120" (0.000112)
1109. feature "NEFL" (0.000112)
1110. feature "PA2G4P6" (0.000112)
1111. feature "SUCLG2" (0.000112)
1112. feature "PPM1A" (0.000112)
1113. feature "PCIF1" (0.000112)
1114. feature "TBL1X" (0.000111)
1115. feature "PDIA5" (0.000111)
1116. feature "ARMC6" (0.000111)
1117. feature "KDM6B" (0.000111)
1118. feature "TBC1D22A" (0.000111)
1119. feature "GLCCI1" (0.000110)
1120. feature "WT1-AS" (0.000110)
1121. feature "TAF1A" (0.000110)
1122. feature "GCAT" (0.000110)
1123. feature "B4GALT2" (0.000110)
1124. feature "POLE2" (0.000110)
1125. feature "PRXL2B" (0.000110)
1126. feature "IMPACT" (0.000110)
1127. feature "ARHGAP42" (0.000109)
1128. feature "SLC40A1" (0.000109)
1129. feature "DNAJB2" (0.000109)
1130. feature "EEF1A2" (0.000109)
1131. feature "FBXO48" (0.000109)
1132. feature "SMIM29" (0.000109)
1133. feature "TCP11L1" (0.000109)
1134. feature "FBXO42" (0.000109)
1135. feature "DAZAP2" (0.000109)
1136. feature "GSK3A" (0.000109)
1137. feature "BEND6" (0.000108)
1138. feature "CDK14" (0.000108)
1139. feature "SS18" (0.000108)
1140. feature "LRRC45" (0.000108)
1141. feature "ARHGEF28" (0.000108)
1142. feature "MZT1" (0.000107)
1143. feature "MAZ" (0.000107)
1144. feature "MAGI2-AS3" (0.000107)
1145. feature "CTXN1" (0.000107)
1146. feature "VAT1" (0.000107)
1147. feature "TMEM45A" (0.000107)
1148. feature "HMG20B" (0.000107)
1149. feature "BMPR2" (0.000107)
1150. feature "UBXN6" (0.000107)
1151. feature "AMOTL2" (0.000107)
1152. feature "DERA" (0.000106)
1153. feature "CRIP2" (0.000106)
1154. feature "CADM1" (0.000106)
1155. feature "MID1" (0.000106)
1156. feature "PDLIM4" (0.000106)
1157. feature "IDH3G" (0.000106)
1158. feature "MAN2B1" (0.000106)
1159. feature "SNCG" (0.000106)
1160. feature "GEN1" (0.000105)
1161. feature "WAC-AS1" (0.000105)
1162. feature "RPGR" (0.000104)
1163. feature "GRIN2D" (0.000104)
1164. feature "AP1S3" (0.000104)
1165. feature "RPL22L1" (0.000104)
1166. feature "IBA57" (0.000104)
1167. feature "HAUS5" (0.000104)
1168. feature "IQANK1" (0.000104)
1169. feature "CERS6" (0.000104)
1170. feature "GABPA" (0.000104)
1171. feature "TMEM80" (0.000104)
1172. feature "DIP2C" (0.000104)
1173. feature "POLG" (0.000103)
1174. feature "CTDSP2" (0.000103)
1175. feature "SMIM26" (0.000103)
1176. feature "TYMP" (0.000103)
1177. feature "SH3BP1" (0.000103)
1178. feature "CLEC11A" (0.000103)
1179. feature "IFRD2" (0.000103)
1180. feature "FAM83A-AS1" (0.000103)
1181. feature "OGDH" (0.000103)
1182. feature "EZH1" (0.000102)
1183. feature "LRAT" (0.000102)
1184. feature "HDAC7" (0.000102)
1185. feature "RHNO1" (0.000102)
1186. feature "CCNY" (0.000102)
1187. feature "DYNLT1" (0.000102)
1188. feature "GNB2" (0.000102)
1189. feature "CLASRP" (0.000102)
1190. feature "MUL1" (0.000102)
1191. feature "BCL2L12" (0.000102)
1192. feature "MIXL1" (0.000101)
1193. feature "CCDC92" (0.000101)
1194. feature "HSBP1L1" (0.000101)
1195. feature "PCDHB1" (0.000101)
1196. feature "CHTF18" (0.000101)
1197. feature "FYCO1" (0.000100)
1198. feature "TFE3" (0.000100)
1199. feature "PLEKHG4B" (0.000100)
1200. feature "SFTA1P" (0.000100)
1201. feature "CCDC26" (0.000100)
1202. feature "VPS52" (0.000100)
1203. feature "FBXO30" (0.000099)
1204. feature "SHMT1" (0.000099)
1205. feature "CLEC4OP" (0.000099)
1206. feature "PISD" (0.000099)
1207. feature "ALG13" (0.000099)
1208. feature "C16orf91" (0.000099)
1209. feature "PLP2" (0.000099)
1210. feature "GATA2-AS1" (0.000099)
1211. feature "GAS8" (0.000099)
1212. feature "CRYZL1" (0.000099)
1213. feature "CCDC74A" (0.000099)
1214. feature "RAVER1" (0.000099)
1215. feature "ATP5MC2" (0.000099)
1216. feature "ITM2C" (0.000099)
1217. feature "LDLRAP1" (0.000098)
1218. feature "RNPEPL1" (0.000098)
1219. feature "IGFBP7" (0.000097)
1220. feature "VAMP2" (0.000097)
1221. feature "CHMP1A" (0.000097)
1222. feature "RABGGTA" (0.000097)
1223. feature "ZNF654" (0.000097)
1224. feature "RETREG2" (0.000096)
1225. feature "PIKFYVE" (0.000096)
1226. feature "MCRIP1" (0.000096)
1227. feature "ZNF581" (0.000096)
1228. feature "XRCC1" (0.000096)
1229. feature "RTL6" (0.000096)
1230. feature "TMEM250" (0.000096)
1231. feature "ST8SIA6-AS1" (0.000096)
1232. feature "ZNF184" (0.000096)
1233. feature "POLR2H" (0.000095)
1234. feature "VPS54" (0.000095)
1235. feature "KCTD1" (0.000095)
1236. feature "C1orf115" (0.000094)
1237. feature "RNF25" (0.000094)
1238. feature "C9orf64" (0.000094)
1239. feature "NEBL" (0.000094)
1240. feature "PLIN3" (0.000094)
1241. feature "RHBDL2" (0.000094)
1242. feature "SNHG18" (0.000094)
1243. feature "PPIL1" (0.000093)
1244. feature "C15orf39" (0.000092)
1245. feature "KIF9" (0.000092)
1246. feature "ZBED2" (0.000092)
1247. feature "BICRA" (0.000092)
1248. feature "BCL7B" (0.000092)
1249. feature "TMEM25" (0.000092)
1250. feature "JMJD6" (0.000092)
1251. feature "HIP1R" (0.000092)
1252. feature "SMIM1" (0.000092)
1253. feature "WDR90" (0.000092)
1254. feature "ARHGEF39" (0.000091)
1255. feature "WDR74" (0.000091)
1256. feature "TBC1D16" (0.000091)
1257. feature "NOA1" (0.000091)
1258. feature "CASP6" (0.000091)
1259. feature "RAB8B" (0.000091)
1260. feature "RPA1" (0.000091)
1261. feature "TFIP11" (0.000091)
1262. feature "SURF1" (0.000090)
1263. feature "HEY1" (0.000090)
1264. feature "GIN1" (0.000090)
1265. feature "SUN2" (0.000090)
1266. feature "ADARB1" (0.000090)
1267. feature "SPP1" (0.000090)
1268. feature "FASTKD5" (0.000090)
1269. feature "CACTIN" (0.000090)
1270. feature "TXNRD2" (0.000090)
1271. feature "DLX6" (0.000089)
1272. feature "CRNDE" (0.000089)
1273. feature "LINC01116" (0.000089)
1274. feature "CASP3" (0.000089)
1275. feature "FASTK" (0.000089)
1276. feature "CDCA4" (0.000089)
1277. feature "KRT15" (0.000089)
1278. feature "PHLPP1" (0.000088)
1279. feature "YY1AP1" (0.000088)
1280. feature "EPHX1" (0.000088)
1281. feature "MREG" (0.000088)
1282. feature "DPRX" (0.000088)
1283. feature "BACE2" (0.000088)
1284. feature "NEK6" (0.000088)
1285. feature "MICAL2" (0.000088)
1286. feature "SH2B1" (0.000088)
1287. feature "AAMDC" (0.000088)
1288. feature "SNORD3B-1" (0.000087)
1289. feature "MT-TL1" (0.000087)
1290. feature "PHEX" (0.000087)
1291. feature "MIR200CHG" (0.000087)
1292. feature "FAM83G" (0.000087)
1293. feature "ARHGEF18" (0.000087)
1294. feature "XPNPEP1" (0.000087)
1295. feature "KHK" (0.000087)
1296. feature "SRGAP2" (0.000087)
1297. feature "DCAKD" (0.000087)
1298. feature "TRAK1" (0.000086)
1299. feature "PHACTR1" (0.000086)
1300. feature "FOXJ2" (0.000086)
1301. feature "ZDHHC4" (0.000086)
1302. feature "BAHD1" (0.000086)
1303. feature "SLC25A28" (0.000086)
1304. feature "ZBTB2" (0.000086)
1305. feature "CYHR1" (0.000086)
1306. feature "LCA5" (0.000086)
1307. feature "CRYBB2" (0.000085)
1308. feature "PLEKHF1" (0.000085)
1309. feature "CXXC5" (0.000085)
1310. feature "FANCE" (0.000085)
1311. feature "AKAP5" (0.000085)
1312. feature "DVL1" (0.000085)
1313. feature "BORCS6" (0.000084)
1314. feature "HEBP1" (0.000084)
1315. feature "LINC01151" (0.000084)
1316. feature "TEX30" (0.000084)
1317. feature "MBD1" (0.000083)
1318. feature "C19orf47" (0.000083)
1319. feature "APBA2" (0.000083)
1320. feature "TATDN2" (0.000083)
1321. feature "TNFRSF10B" (0.000083)
1322. feature "TSEN54" (0.000083)
1323. feature "FAM120C" (0.000083)
1324. feature "RPL21P10" (0.000082)
1325. feature "IPMK" (0.000082)
1326. feature "AP1S2" (0.000082)
1327. feature "ADAP1" (0.000082)
1328. feature "AFAP1L2" (0.000082)
1329. feature "RSPH3" (0.000082)
1330. feature "ZNF22" (0.000082)
1331. feature "RHOV" (0.000082)
1332. feature "UNC119B" (0.000082)
1333. feature "AIP" (0.000081)
1334. feature "SNHG19" (0.000081)
1335. feature "E2F1" (0.000081)
1336. feature "FNDC10" (0.000081)
1337. feature "NECTIN2" (0.000081)
1338. feature "USP32" (0.000081)
1339. feature "ERG28" (0.000081)
1340. feature "TBC1D7" (0.000081)
1341. feature "CLIP2" (0.000081)
1342. feature "SRSF8" (0.000081)
1343. feature "ELF4" (0.000080)
1344. feature "AMZ2P1" (0.000080)
1345. feature "FBXL19" (0.000080)
1346. feature "GALNS" (0.000080)
1347. feature "PDPK1" (0.000080)
1348. feature "SLC39A11" (0.000080)
1349. feature "B3GALT6" (0.000080)
1350. feature "ARMH3" (0.000080)
1351. feature "BET1" (0.000079)
1352. feature "PRR15" (0.000079)
1353. feature "C16orf74" (0.000079)
1354. feature "ELP2" (0.000079)
1355. feature "HIGD1A" (0.000079)
1356. feature "ANXA4" (0.000079)
1357. feature "UBQLN2" (0.000079)
1358. feature "SELENOO" (0.000079)
1359. feature "LRRIQ1" (0.000078)
1360. feature "SLC2A11" (0.000078)
1361. feature "FBXL15" (0.000078)
1362. feature "MSX1" (0.000078)
1363. feature "CCNG1" (0.000078)
1364. feature "PITPNM1" (0.000078)
1365. feature "PTPN6" (0.000078)
1366. feature "FAM189B" (0.000078)
1367. feature "ACSF3" (0.000078)
1368. feature "ANKRD27" (0.000078)
1369. feature "ZNF195" (0.000078)
1370. feature "STUB1" (0.000078)
1371. feature "AAAS" (0.000078)
1372. feature "OPRM1" (0.000078)
1373. feature "SP2" (0.000078)
1374. feature "ZDHHC7" (0.000077)
1375. feature "BICC1" (0.000077)
1376. feature "NTN4" (0.000077)
1377. feature "SEMA3D" (0.000077)
1378. feature "NCOA1" (0.000077)
1379. feature "TRIM8" (0.000077)
1380. feature "NKX3-1" (0.000076)
1381. feature "SBNO2" (0.000076)
1382. feature "PGBD1" (0.000076)
1383. feature "WDR6" (0.000076)
1384. feature "BCAR1" (0.000076)
1385. feature "WDR53" (0.000076)
1386. feature "OTULINL" (0.000076)
1387. feature "DOHH" (0.000075)
1388. feature "NT5C" (0.000075)
1389. feature "FABP6" (0.000075)
1390. feature "PCYT1B" (0.000075)
1391. feature "FPGS" (0.000075)
1392. feature "GM2A" (0.000075)
1393. feature "ZCWPW1" (0.000075)
1394. feature "PSMD9" (0.000075)
1395. feature "DYSF" (0.000074)
1396. feature "GAS2L1" (0.000074)
1397. feature "KDM5C" (0.000074)
1398. feature "RFK" (0.000074)
1399. feature "FAM114A2" (0.000074)
1400. feature "IL6R" (0.000074)
1401. feature "FYN" (0.000074)
1402. feature "XYLB" (0.000074)
1403. feature "FGF8" (0.000073)
1404. feature "DNAH11" (0.000073)
1405. feature "TTC3P1" (0.000073)
1406. feature "TRIM52-AS1" (0.000073)
1407. feature "MAML2" (0.000073)
1408. feature "KIF3C" (0.000073)
1409. feature "GNG4" (0.000073)
1410. feature "JAKMIP3" (0.000072)
1411. feature "PKN1" (0.000072)
1412. feature "ZNF470" (0.000072)
1413. feature "LINC00342" (0.000072)
1414. feature "FAM20C" (0.000072)
1415. feature "PTPRU" (0.000072)
1416. feature "PANX2" (0.000072)
1417. feature "CROCC" (0.000072)
1418. feature "FBXL17" (0.000072)
1419. feature "ULK2" (0.000072)
1420. feature "LHPP" (0.000072)
1421. feature "ARNTL" (0.000072)
1422. feature "MEX3D" (0.000071)
1423. feature "DHRS4" (0.000071)
1424. feature "ZNF558" (0.000071)
1425. feature "ZNF33B" (0.000071)
1426. feature "DCAF10" (0.000071)
1427. feature "ZXDC" (0.000071)
1428. feature "ANKEF1" (0.000071)
1429. feature "LINC02210" (0.000071)
1430. feature "GPR153" (0.000071)
1431. feature "IFFO2" (0.000071)
1432. feature "EGLN2" (0.000070)
1433. feature "FGFR3" (0.000070)
1434. feature "CMTM4" (0.000070)
1435. feature "CRPPA" (0.000070)
1436. feature "MT-TN" (0.000070)
1437. feature "PI4KB" (0.000070)
1438. feature "SETD1A" (0.000070)
1439. feature "DHCR7" (0.000070)
1440. feature "BIN1" (0.000069)
1441. feature "MYBL1" (0.000069)
1442. feature "TGFB1I1" (0.000069)
1443. feature "SYDE1" (0.000069)
1444. feature "THNSL1" (0.000069)
1445. feature "TMPRSS13" (0.000069)
1446. feature "TCHH" (0.000069)
1447. feature "YPEL5" (0.000069)
1448. feature "SIX3" (0.000069)
1449. feature "LINC01322" (0.000069)
1450. feature "DOCK9" (0.000068)
1451. feature "IGSF6" (0.000068)
1452. feature "FAM13B" (0.000068)
1453. feature "HAUS7" (0.000068)
1454. feature "NECAP1" (0.000068)
1455. feature "C11orf49" (0.000068)
1456. feature "VDR" (0.000068)
1457. feature "PLK4" (0.000068)
1458. feature "TMEM39B" (0.000068)
1459. feature "ARSA" (0.000068)
1460. feature "HELQ" (0.000068)
1461. feature "ZNF165" (0.000068)
1462. feature "TRAPPC2B" (0.000068)
1463. feature "R3HCC1" (0.000067)
1464. feature "PRECSIT" (0.000067)
1465. feature "CDR2" (0.000067)
1466. feature "PGPEP1" (0.000067)
1467. feature "RCHY1" (0.000067)
1468. feature "PLCG1" (0.000067)
1469. feature "KCTD2" (0.000067)
1470. feature "ABHD17A" (0.000067)
1471. feature "SIX2" (0.000067)
1472. feature "C1QTNF6" (0.000067)
1473. feature "SNX22" (0.000066)
1474. feature "RAD9A" (0.000066)
1475. feature "ESR1" (0.000066)
1476. feature "ABHD10" (0.000066)
1477. feature "NTAN1" (0.000066)
1478. feature "HPRT1" (0.000066)
1479. feature "LTBP3" (0.000066)
1480. feature "RTL8C" (0.000066)
1481. feature "PXMP4" (0.000066)
1482. feature "ZNF718" (0.000066)
1483. feature "WARS2" (0.000066)
1484. feature "TEAD3" (0.000066)
1485. feature "COPZ2" (0.000066)
1486. feature "MFSD5" (0.000065)
1487. feature "HAS3" (0.000065)
1488. feature "MAP3K6" (0.000065)
1489. feature "STYX" (0.000065)
1490. feature "MYPOP" (0.000065)
1491. feature "RAB20" (0.000065)
1492. feature "SPATC1L" (0.000065)
1493. feature "SECISBP2L" (0.000065)
1494. feature "ABT1" (0.000065)
1495. feature "GPRC5D-AS1" (0.000065)
1496. feature "C19orf54" (0.000065)
1497. feature "DBF4B" (0.000064)
1498. feature "SDHAF3" (0.000064)
1499. feature "NDST1" (0.000064)
1500. feature "ALDH3B1" (0.000064)
1501. feature "MT-TA" (0.000064)
1502. feature "NAALADL2" (0.000064)
1503. feature "UBL4A" (0.000064)
1504. feature "PTK2B" (0.000064)
1505. feature "PYGO1" (0.000064)
1506. feature "DECR2" (0.000064)
1507. feature "GPM6B" (0.000063)
1508. feature "RNF19B" (0.000063)
1509. feature "SELENON" (0.000063)
1510. feature "POLR3GL" (0.000063)
1511. feature "UBALD2" (0.000062)
1512. feature "PID1" (0.000062)
1513. feature "NAIP" (0.000062)
1514. feature "GSDME" (0.000062)
1515. feature "SESN2" (0.000062)
1516. feature "PLAC8" (0.000062)
1517. feature "MAP1A" (0.000062)
1518. feature "MAPK8" (0.000062)
1519. feature "MTO1" (0.000062)
1520. feature "BTBD7P1" (0.000062)
1521. feature "ZNF628" (0.000062)
1522. feature "PATZ1" (0.000062)
1523. feature "SLC25A53" (0.000062)
1524. feature "CTTN-DT" (0.000062)
1525. feature "ILRUN-AS1" (0.000062)
1526. feature "MAPK8IP3" (0.000061)
1527. feature "SPN" (0.000061)
1528. feature "PPP1R14C" (0.000061)
1529. feature "INKA1" (0.000061)
1530. feature "MAPK12" (0.000060)
1531. feature "NAXD" (0.000060)
1532. feature "C12orf60" (0.000060)
1533. feature "PRKCD" (0.000060)
1534. feature "NCK2" (0.000060)
1535. feature "WWC1" (0.000060)
1536. feature "ZBTB34" (0.000060)
1537. feature "SIRT5" (0.000060)
1538. feature "ZNF850" (0.000060)
1539. feature "TIMM29" (0.000060)
1540. feature "STARD4" (0.000060)
1541. feature "BIN3" (0.000060)
1542. feature "CHN1" (0.000059)
1543. feature "LINC02484" (0.000059)
1544. feature "STAT2" (0.000059)
1545. feature "PEX11A" (0.000059)
1546. feature "RAP1GAP2" (0.000059)
1547. feature "HK2" (0.000059)
1548. feature "CASP1" (0.000059)
1549. feature "PRKCH" (0.000059)
1550. feature "NEU1" (0.000059)
1551. feature "CDK5" (0.000059)
1552. feature "AKT1" (0.000059)
1553. feature "DTX2" (0.000059)
1554. feature "B3GLCT" (0.000059)
1555. feature "UMAD1" (0.000059)
1556. feature "FRG1JP" (0.000058)
1557. feature "LRRC8B" (0.000058)
1558. feature "LPP-AS2" (0.000058)
1559. feature "POLR2J3" (0.000058)
1560. feature "OBSL1" (0.000058)
1561. feature "TJAP1" (0.000058)
1562. feature "FAM107A" (0.000058)
1563. feature "CHMP7" (0.000058)
1564. feature "TRMT2A" (0.000058)
1565. feature "LINC02428" (0.000057)
1566. feature "RAB36" (0.000057)
1567. feature "NAA40" (0.000057)
1568. feature "PRELID2" (0.000057)
1569. feature "UBTD1" (0.000057)
1570. feature "TXNDC11" (0.000057)
1571. feature "TRIM21" (0.000057)
1572. feature "SPINK5" (0.000057)
1573. feature "SLC35B3" (0.000057)
1574. feature "DYRK4" (0.000057)
1575. feature "EPB41L4B" (0.000057)
1576. feature "CARD16" (0.000057)
1577. feature "KLC3" (0.000057)
1578. feature "KIFC3" (0.000057)
1579. feature "SMARCAL1" (0.000056)
1580. feature "ATP6V1B2" (0.000056)
1581. feature "LINC01186" (0.000056)
1582. feature "EML1" (0.000056)
1583. feature "EIF3J-DT" (0.000056)
1584. feature "PER3" (0.000056)
1585. feature "OXNAD1" (0.000056)
1586. feature "MANBA" (0.000056)
1587. feature "ZNF524" (0.000056)
1588. feature "RPL21P34" (0.000056)
1589. feature "UGT1A7" (0.000056)
1590. feature "GMEB2" (0.000056)
1591. feature "VPS9D1" (0.000056)
1592. feature "ARHGAP22" (0.000055)
1593. feature "ZFYVE27" (0.000055)
1594. feature "JADE1" (0.000055)
1595. feature "ZNF250" (0.000055)
1596. feature "THAP4" (0.000055)
1597. feature "CCDC136" (0.000055)
1598. feature "POLA2" (0.000055)
1599. feature "LYSMD1" (0.000055)
1600. feature "INPP5E" (0.000055)
1601. feature "ZDHHC18" (0.000055)
1602. feature "MAP3K12" (0.000055)
1603. feature "SENP3" (0.000055)
1604. feature "PORCN" (0.000055)
1605. feature "RAB40C" (0.000055)
1606. feature "TWIST1" (0.000055)
1607. feature "DELE1" (0.000055)
1608. feature "MRTFA" (0.000055)
1609. feature "SELENOP" (0.000054)
1610. feature "KCTD15" (0.000054)
1611. feature "ZBTB10" (0.000054)
1612. feature "DOP1A" (0.000054)
1613. feature "PALM3" (0.000054)
1614. feature "MED18" (0.000054)
1615. feature "MTFP1" (0.000054)
1616. feature "C3orf14" (0.000054)
1617. feature "WDR27" (0.000054)
1618. feature "MRPS36" (0.000054)
1619. feature "RPL12P13" (0.000054)
1620. feature "PANX1" (0.000054)
1621. feature "BOLA3-AS1" (0.000054)
1622. feature "ATP6V0C" (0.000054)
1623. feature "HNF4A" (0.000053)
1624. feature "KLHDC3" (0.000053)
1625. feature "HFE" (0.000053)
1626. feature "S1PR3" (0.000053)
1627. feature "FSD1L" (0.000053)
1628. feature "C16orf95-DT" (0.000053)
1629. feature "AGO4" (0.000053)
1630. feature "RBP1" (0.000053)
1631. feature "ZNF880" (0.000053)
1632. feature "CRLF1" (0.000053)
1633. feature "BRSK2" (0.000053)
1634. feature "LINC02870" (0.000053)
1635. feature "SHANK2" (0.000053)
1636. feature "ATP5PDP3" (0.000053)
1637. feature "ZNF251" (0.000053)
1638. feature "MMP25-AS1" (0.000053)
1639. feature "TMEM44-AS1" (0.000053)
1640. feature "RAB11FIP5" (0.000053)
1641. feature "ZNF516" (0.000052)
1642. feature "NAT14" (0.000052)
1643. feature "WASF3" (0.000052)
1644. feature "ZNF777" (0.000052)
1645. feature "PLEKHO1" (0.000052)
1646. feature "EPB41L4A-AS1" (0.000052)
1647. feature "CPNE2" (0.000052)
1648. feature "FUT11" (0.000052)
1649. feature "FBXL3" (0.000052)
1650. feature "GLB1" (0.000052)
1651. feature "ZBTB26" (0.000052)
1652. feature "RNASEH1-AS1" (0.000052)
1653. feature "DTWD2" (0.000052)
1654. feature "NRXN3" (0.000052)
1655. feature "CTDP1" (0.000052)
1656. feature "TEDC2" (0.000052)
1657. feature "RBM43" (0.000052)
1658. feature "CACNB3" (0.000051)
1659. feature "PTPDC1" (0.000051)
1660. feature "FKTN" (0.000051)
1661. feature "MTND4P12" (0.000051)
1662. feature "MAP3K1" (0.000051)
1663. feature "OPN3" (0.000051)
1664. feature "TAB1" (0.000051)
1665. feature "DPY19L1" (0.000051)
1666. feature "ACYP2" (0.000051)
1667. feature "PER1" (0.000051)
1668. feature "LINC01962" (0.000051)
1669. feature "FOXN3-AS1" (0.000051)
1670. feature "EIPR1" (0.000051)
1671. feature "PIK3R2" (0.000051)
1672. feature "INTS14" (0.000051)
1673. feature "ERP27" (0.000051)
1674. feature "ALS2CL" (0.000051)
1675. feature "FBXO38" (0.000050)
1676. feature "MACROH2A2" (0.000050)
1677. feature "CAMKK1" (0.000050)
1678. feature "NSMCE3" (0.000050)
1679. feature "NRARP" (0.000050)
1680. feature "RHBDF1" (0.000050)
1681. feature "TMEM52" (0.000050)
1682. feature "DGLUCY" (0.000050)
1683. feature "DMPK" (0.000050)
1684. feature "GJB3" (0.000050)
1685. feature "THAP2" (0.000050)
1686. feature "HAAO" (0.000050)
1687. feature "SAP30L-AS1" (0.000050)
1688. feature "C18orf32" (0.000050)
1689. feature "PIM3" (0.000050)
1690. feature "POLD1" (0.000050)
1691. feature "ZNF629" (0.000050)
1692. feature "CAMSAP3" (0.000050)
1693. feature "FAM228B" (0.000049)
1694. feature "GKAP1" (0.000049)
1695. feature "TKFC" (0.000049)
1696. feature "TPRA1" (0.000049)
1697. feature "CDH8" (0.000049)
1698. feature "RHPN1" (0.000049)
1699. feature "MAFK" (0.000049)
1700. feature "CAPN10" (0.000049)
1701. feature "MIR210HG" (0.000049)
1702. feature "BTG2" (0.000048)
1703. feature "SAP30L" (0.000048)
1704. feature "LATS2" (0.000048)
1705. feature "SQOR" (0.000048)
1706. feature "MELTF-AS1" (0.000048)
1707. feature "RUNX2" (0.000048)
1708. feature "RASSF7" (0.000048)
1709. feature "HMGN1P26" (0.000048)
1710. feature "PROSER2" (0.000048)
1711. feature "SLC4A2" (0.000048)
1712. feature "TIGD5" (0.000048)
1713. feature "TCF15" (0.000048)
1714. feature "CELSR1" (0.000048)
1715. feature "ORMDL3" (0.000048)
1716. feature "RGL2" (0.000048)
1717. feature "ZCCHC3" (0.000048)
1718. feature "NFKBIL1" (0.000048)
1719. feature "TSGA10" (0.000048)
1720. feature "PRIMPOL" (0.000047)
1721. feature "PLEKHA8P1" (0.000047)
1722. feature "LINC02076" (0.000047)
1723. feature "UVSSA" (0.000047)
1724. feature "CCDC71L" (0.000047)
1725. feature "RBBP8NL" (0.000047)
1726. feature "SAP30-DT" (0.000047)
1727. feature "PIH1D2" (0.000047)
1728. feature "NLE1" (0.000047)
1729. feature "RTL8A" (0.000047)
1730. feature "KBTBD3" (0.000047)
1731. feature "NUDT8" (0.000047)
1732. feature "CPE" (0.000047)
1733. feature "ZBTB12" (0.000047)
1734. feature "NIF3L1" (0.000047)
1735. feature "FAM171B" (0.000047)
1736. feature "PKN3" (0.000046)
1737. feature "SLC25A22" (0.000046)
1738. feature "MTCO1P12" (0.000046)
1739. feature "KCP" (0.000046)
1740. feature "FANCF" (0.000046)
1741. feature "UNK" (0.000046)
1742. feature "TRIB2" (0.000046)
1743. feature "HAP1" (0.000046)
1744. feature "RTN4R" (0.000046)
1745. feature "DPYSL5" (0.000046)
1746. feature "CCDC102A" (0.000046)
1747. feature "HOXA11-AS" (0.000046)
1748. feature "S100A4" (0.000046)
1749. feature "SMTN" (0.000046)
1750. feature "GAS6-DT" (0.000046)
1751. feature "MED23" (0.000046)
1752. feature "MXD3" (0.000046)
1753. feature "NIPSNAP3A" (0.000046)
1754. feature "ZNF117" (0.000045)
1755. feature "VPS16" (0.000045)
1756. feature "MFAP2" (0.000045)
1757. feature "DTD2" (0.000045)
1758. feature "CHMP6" (0.000045)
1759. feature "MOCOS" (0.000045)
1760. feature "COL27A1" (0.000045)
1761. feature "SAMD15" (0.000045)
1762. feature "HOXC13" (0.000045)
1763. feature "MIB2" (0.000045)
1764. feature "HES2" (0.000045)
1765. feature "CNFN" (0.000045)
1766. feature "MAN1B1-DT" (0.000045)
1767. feature "IFNGR2" (0.000045)
1768. feature "TMEM94" (0.000045)
1769. feature "ERG" (0.000045)
1770. feature "SYVN1" (0.000045)
1771. feature "SLC16A4" (0.000045)
1772. feature "HLA-F" (0.000044)
1773. feature "CST6" (0.000044)
1774. feature "TRIM39" (0.000044)
1775. feature "SSPN" (0.000044)
1776. feature "DUSP5" (0.000044)
1777. feature "GSEC" (0.000044)
1778. feature "BDKRB2" (0.000044)
1779. feature "KIF26A" (0.000044)
1780. feature "MFSD13A" (0.000044)
1781. feature "PIGC" (0.000044)
1782. feature "SMG8" (0.000044)
1783. feature "FOXD2" (0.000044)
1784. feature "ZFP30" (0.000044)
1785. feature "PTRHD1" (0.000044)
1786. feature "BAIAP2-DT" (0.000044)
1787. feature "RALGDS" (0.000044)
1788. feature "E4F1" (0.000044)
1789. feature "PODXL2" (0.000043)
1790. feature "ZNF446" (0.000043)
1791. feature "EAF2" (0.000043)
1792. feature "TMEM184B" (0.000043)
1793. feature "PTPN21" (0.000043)
1794. feature "MCEE" (0.000043)
1795. feature "RNF170" (0.000043)
1796. feature "CCDC88B" (0.000043)
1797. feature "COL5A1" (0.000043)
1798. feature "PAX5" (0.000043)
1799. feature "KAT14" (0.000043)
1800. feature "MTATP6P1" (0.000043)
1801. feature "MSL3P1" (0.000043)
1802. feature "KLHL22" (0.000043)
1803. feature "PIGK" (0.000043)
1804. feature "OR7E14P" (0.000043)
1805. feature "ZKSCAN2" (0.000043)
1806. feature "LRRC37A3" (0.000043)
1807. feature "MCM3AP-AS1" (0.000042)
1808. feature "MVK" (0.000042)
1809. feature "PLD2" (0.000042)
1810. feature "PSMG3-AS1" (0.000042)
1811. feature "ADA" (0.000042)
1812. feature "P3H1" (0.000042)
1813. feature "TAFAZZIN" (0.000042)
1814. feature "SMIM20" (0.000042)
1815. feature "DPPA2" (0.000042)
1816. feature "TRAF4" (0.000042)
1817. feature "TROAP" (0.000042)
1818. feature "KHNYN" (0.000042)
1819. feature "PARP10" (0.000042)
1820. feature "NLRX1" (0.000042)
1821. feature "RRAGA" (0.000042)
1822. feature "CFD" (0.000042)
1823. feature "TAGAP-AS1" (0.000042)
1824. feature "CDK9" (0.000042)
1825. feature "ALDOC" (0.000042)
1826. feature "KRT3" (0.000042)
1827. feature "FAH" (0.000042)
1828. feature "CXCL8" (0.000042)
1829. feature "ANO7" (0.000042)
1830. feature "TEX9" (0.000042)
1831. feature "DENND3-AS1" (0.000042)
1832. feature "TBC1D13" (0.000042)
1833. feature "SIAH1" (0.000041)
1834. feature "ITGB8-AS1" (0.000041)
1835. feature "TMEM143" (0.000041)
1836. feature "ZG16B" (0.000041)
1837. feature "B4GALT7" (0.000041)
1838. feature "GLMP" (0.000041)
1839. feature "SUPT3H" (0.000041)
1840. feature "OCRL" (0.000041)
1841. feature "HMBOX1" (0.000041)
1842. feature "KCNG1" (0.000041)
1843. feature "ABCA2" (0.000041)
1844. feature "C1orf53" (0.000041)
1845. feature "FAM169A" (0.000041)
1846. feature "HEY2" (0.000041)
1847. feature "ULBP1" (0.000041)
1848. feature "UCKL1" (0.000041)
1849. feature "TEF" (0.000041)
1850. feature "LOXL2" (0.000041)
1851. feature "ZNF865" (0.000041)
1852. feature "GNAS-AS1" (0.000040)
1853. feature "RASGEF1A" (0.000040)
1854. feature "NRGN" (0.000040)
1855. feature "ADNP-AS1" (0.000040)
1856. feature "NRSN2-AS1" (0.000040)
1857. feature "TANGO2" (0.000040)
1858. feature "ZYG11A" (0.000040)
1859. feature "HOXC13-AS" (0.000040)
1860. feature "ZNF407-AS1" (0.000040)
1861. feature "RECQL5" (0.000040)
1862. feature "CRLS1" (0.000040)
1863. feature "PCED1A" (0.000040)
1864. feature "C6orf136" (0.000040)
1865. feature "EVI5L" (0.000040)
1866. feature "EXOSC10-AS1" (0.000040)
1867. feature "TRPM2" (0.000040)
1868. feature "MMAA" (0.000040)
1869. feature "COMMD10" (0.000040)
1870. feature "HYAL3" (0.000040)
1871. feature "PCDHGC3" (0.000039)
1872. feature "SH3D21" (0.000039)
1873. feature "JPH3" (0.000039)
1874. feature "GABRE" (0.000039)
1875. feature "CEACAM19" (0.000039)
1876. feature "FBXO4" (0.000039)
1877. feature "AFAP1L1" (0.000039)
1878. feature "ZNF710" (0.000039)
1879. feature "RAPGEF3" (0.000039)
1880. feature "ZFP28" (0.000039)
1881. feature "TSHZ2" (0.000039)
1882. feature "PKD1L1" (0.000039)
1883. feature "ARL9" (0.000038)
1884. feature "ZNF319" (0.000038)
1885. feature "PKMYT1" (0.000038)
1886. feature "TMEM121" (0.000038)
1887. feature "SLC26A6" (0.000038)
1888. feature "B4GAT1" (0.000038)
1889. feature "HLA-DPB1" (0.000038)
1890. feature "MOGS" (0.000038)
1891. feature "NAB2" (0.000038)
1892. feature "USP35" (0.000038)
1893. feature "MEX3C" (0.000038)
1894. feature "ALPK1" (0.000038)
1895. feature "LINC02225" (0.000038)
1896. feature "MAGEA12" (0.000038)
1897. feature "DOCK6" (0.000038)
1898. feature "ZNF395" (0.000038)
1899. feature "REPS2" (0.000038)
1900. feature "ZNF784" (0.000038)
1901. feature "IL4R" (0.000038)
1902. feature "SYT7" (0.000038)
1903. feature "FBXO2" (0.000038)
1904. feature "SNX18" (0.000037)
1905. feature "TP73-AS1" (0.000037)
1906. feature "C1orf112" (0.000037)
1907. feature "ISOC1" (0.000037)
1908. feature "SLC22A5" (0.000037)
1909. feature "IER5L" (0.000037)
1910. feature "NEK3" (0.000037)
1911. feature "PXDN" (0.000037)
1912. feature "PLEKHA7" (0.000037)
1913. feature "IL12B" (0.000037)
1914. feature "IL17RC" (0.000037)
1915. feature "DEPDC1B" (0.000037)
1916. feature "LINC01559" (0.000037)
1917. feature "CALCOCO1" (0.000037)
1918. feature "ZNF335" (0.000037)
1919. feature "TIGD1" (0.000037)
1920. feature "PDE6D" (0.000037)
1921. feature "LINC02080" (0.000037)
1922. feature "PBXIP1" (0.000037)
1923. feature "LINC00960" (0.000037)
1924. feature "PKIB" (0.000037)
1925. feature "SMIM4" (0.000037)
1926. feature "ANKRA2" (0.000037)
1927. feature "IFIT1" (0.000036)
1928. feature "TBCCD1" (0.000036)
1929. feature "KRT18P59" (0.000036)
1930. feature "NRG4" (0.000036)
1931. feature "TMTC2" (0.000036)
1932. feature "PEX11B" (0.000036)
1933. feature "CCN2" (0.000036)
1934. feature "SREBF1" (0.000036)
1935. feature "EFNA1" (0.000036)
1936. feature "VPS51" (0.000036)
1937. feature "GLI4" (0.000036)
1938. feature "SLC9A8" (0.000036)
1939. feature "PPP1R3G" (0.000036)
1940. feature "CDPF1" (0.000036)
1941. feature "BEX5" (0.000036)
1942. feature "SH3BGRL2" (0.000036)
1943. feature "GJD2" (0.000036)
1944. feature "OAZ3" (0.000036)
1945. feature "NDOR1" (0.000036)
1946. feature "C10orf88" (0.000036)
1947. feature "CRYM-AS1" (0.000036)
1948. feature "SDAD1P1" (0.000036)
1949. feature "ZNF875" (0.000035)
1950. feature "MTLN" (0.000035)
1951. feature "SHFL" (0.000035)
1952. feature "TLL1" (0.000035)
1953. feature "UCK1" (0.000035)
1954. feature "DOP1B" (0.000035)
1955. feature "PIM1" (0.000035)
1956. feature "NPL" (0.000035)
1957. feature "PTCH1" (0.000035)
1958. feature "ASB7" (0.000035)
1959. feature "OR5M2P" (0.000035)
1960. feature "ZNF132" (0.000035)
1961. feature "FAM234B" (0.000035)
1962. feature "LRFN4" (0.000035)
1963. feature "TMEM179B" (0.000035)
1964. feature "ZNF74" (0.000035)
1965. feature "NEU3" (0.000034)
1966. feature "CHIC2" (0.000034)
1967. feature "TRPV4" (0.000034)
1968. feature "PIGBOS1" (0.000034)
1969. feature "MAGEA3" (0.000034)
1970. feature "CCDC51" (0.000034)
1971. feature "GORASP1" (0.000034)
1972. feature "ZFP3" (0.000034)
1973. feature "ELFN2" (0.000034)
1974. feature "RHOB" (0.000034)
1975. feature "CNKSR1" (0.000034)
1976. feature "TXNRD3" (0.000034)
1977. feature "ZNF542P" (0.000034)
1978. feature "HLA-G" (0.000034)
1979. feature "DLK2" (0.000034)
1980. feature "SWSAP1" (0.000034)
1981. feature "DARS1-AS1" (0.000034)
1982. feature "KDM4C" (0.000034)
1983. feature "ZNF23" (0.000034)
1984. feature "FMC1" (0.000034)
1985. feature "DLX2" (0.000034)
1986. feature "DUSP12" (0.000033)
1987. feature "PRDM11" (0.000033)
1988. feature "KHDC1" (0.000033)
1989. feature "ARL10" (0.000033)
1990. feature "SLC29A2" (0.000033)
1991. feature "NINL" (0.000033)
1992. feature "BTBD6" (0.000033)
1993. feature "SLC30A2" (0.000033)
1994. feature "AKR1B10" (0.000033)
1995. feature "SSH3" (0.000033)
1996. feature "ABHD14A" (0.000033)
1997. feature "SLC52A1" (0.000033)
1998. feature "ADGRL3" (0.000033)
1999. feature "SREBF2-AS1" (0.000033)
2000. feature "DNHD1" (0.000033)
2001. feature "PIP4P2" (0.000033)
2002. feature "RAB11B-AS1" (0.000033)
2003. feature "DNAAF9" (0.000033)
2004. feature "ABAT" (0.000033)
2005. feature "PLCD1" (0.000033)
2006. feature "IMMP2L" (0.000033)
2007. feature "TMEM8B" (0.000033)
2008. feature "TSPAN15" (0.000033)
2009. feature "TRIM35" (0.000033)
2010. feature "CCSER1" (0.000033)
2011. feature "D2HGDH" (0.000033)
2012. feature "PRR14" (0.000033)
2013. feature "EXOC3-AS1" (0.000033)
2014. feature "LRATD1" (0.000033)
2015. feature "BMP1" (0.000033)
2016. feature "SLC24A2" (0.000033)
2017. feature "BAMBI" (0.000033)
2018. feature "B3GALT1" (0.000033)
2019. feature "MAPK7" (0.000032)
2020. feature "ZC3H10" (0.000032)
2021. feature "DIPK1A" (0.000032)
2022. feature "ZNF804A" (0.000032)
2023. feature "MYADM" (0.000032)
2024. feature "TESMIN" (0.000032)
2025. feature "TP53I13" (0.000032)
2026. feature "FBXO32" (0.000032)
2027. feature "ITPKC" (0.000032)
2028. feature "PPCDC" (0.000032)
2029. feature "FKBP9P1" (0.000032)
2030. feature "NRF1" (0.000032)
2031. feature "DNLZ" (0.000032)
2032. feature "CRACR2A" (0.000032)
2033. feature "PBX3" (0.000032)
2034. feature "HECA" (0.000032)
2035. feature "CACHD1" (0.000032)
2036. feature "SYTL4" (0.000032)
2037. feature "NMRK1" (0.000032)
2038. feature "GPRC5C" (0.000032)
2039. feature "TUBBP5" (0.000032)
2040. feature "HNRNPCP3" (0.000032)
2041. feature "ARMC2" (0.000032)
2042. feature "NCKIPSD" (0.000032)
2043. feature "DENND4A" (0.000032)
2044. feature "SOX21-AS1" (0.000032)
2045. feature "ITGB6" (0.000032)
2046. feature "DXO" (0.000031)
2047. feature "VGLL1" (0.000031)
2048. feature "MAML1" (0.000031)
2049. feature "NTRK3" (0.000031)
2050. feature "TIAM2" (0.000031)
2051. feature "DIXDC1" (0.000031)
2052. feature "PWRN1" (0.000031)
2053. feature "HERC6" (0.000031)
2054. feature "TEP1" (0.000031)
2055. feature "GBA" (0.000031)
2056. feature "KCTD14" (0.000031)
2057. feature "VPS45" (0.000031)
2058. feature "ZNF514" (0.000031)
2059. feature "MYL9" (0.000031)
2060. feature "DUSP1" (0.000031)
2061. feature "METTL25B" (0.000031)
2062. feature "CSRNP1" (0.000031)
2063. feature "LTBP4" (0.000031)
2064. feature "FHDC1" (0.000031)
2065. feature "TXNDC15" (0.000031)
2066. feature "RNF185" (0.000031)
2067. feature "AMPD2" (0.000030)
2068. feature "PLXND1" (0.000030)
2069. feature "ARVCF" (0.000030)
2070. feature "SUMF1" (0.000030)
2071. feature "ZNF213" (0.000030)
2072. feature "SETMAR" (0.000030)
2073. feature "B9D2" (0.000030)
2074. feature "ZNF202" (0.000030)
2075. feature "C20orf96" (0.000030)
2076. feature "CA9" (0.000030)
2077. feature "BICDL2" (0.000030)
2078. feature "ZNF45" (0.000030)
2079. feature "LINC01931" (0.000030)
2080. feature "FAM193B" (0.000030)
2081. feature "MYOSLID" (0.000030)
2082. feature "JAZF1" (0.000030)
2083. feature "MAPK8IP2" (0.000030)
2084. feature "LINC02609" (0.000030)
2085. feature "MED26" (0.000030)
2086. feature "ROM1" (0.000030)
2087. feature "KIF27" (0.000030)
2088. feature "MRPL10" (0.000030)
2089. feature "PSG2" (0.000030)
2090. feature "NRBP2" (0.000030)
2091. feature "LYPD1" (0.000030)
2092. feature "PI4K2B" (0.000030)
2093. feature "FBLN2" (0.000029)
2094. feature "IQCC" (0.000029)
2095. feature "BOLA1" (0.000029)
2096. feature "SLC27A1" (0.000029)
2097. feature "ESYT3" (0.000029)
2098. feature "CCDC168" (0.000029)
2099. feature "RNLS" (0.000029)
2100. feature "DLG4" (0.000029)
2101. feature "AGK" (0.000029)
2102. feature "KIFC2" (0.000029)
2103. feature "KBTBD7" (0.000029)
2104. feature "MAP3K9-DT" (0.000029)
2105. feature "LINC02057" (0.000029)
2106. feature "LY6G6C" (0.000029)
2107. feature "OR3A3" (0.000029)
2108. feature "CENPBD1" (0.000029)
2109. feature "EGFLAM" (0.000029)
2110. feature "ARL17B" (0.000029)
2111. feature "TRAPPC14" (0.000029)
2112. feature "KLF16" (0.000029)
2113. feature "DSCR8" (0.000029)
2114. feature "ZNF836" (0.000029)
2115. feature "IRF2-DT" (0.000029)
2116. feature "FBF1" (0.000029)
2117. feature "BCAS3" (0.000029)
2118. feature "FGD2" (0.000029)
2119. feature "PITX1-AS1" (0.000029)
2120. feature "GRB14" (0.000029)
2121. feature "KCTD11" (0.000029)
2122. feature "WHAMMP3" (0.000029)
2123. feature "TNK2" (0.000029)
2124. feature "KLHL6" (0.000029)
2125. feature "NCAM2" (0.000029)
2126. feature "NPRL2" (0.000028)
2127. feature "HSF2BP" (0.000028)
2128. feature "PNPLA3" (0.000028)
2129. feature "SH3RF2" (0.000028)
2130. feature "OVOL1" (0.000028)
2131. feature "SERPINB7" (0.000028)
2132. feature "YPEL3" (0.000028)
2133. feature "ARRDC1" (0.000028)
2134. feature "MIATNB" (0.000028)
2135. feature "ALDH5A1" (0.000028)
2136. feature "RASSF10" (0.000028)
2137. feature "ANKRD9" (0.000028)
2138. feature "RPL13AP7" (0.000028)
2139. feature "MTUS2" (0.000028)
2140. feature "TDRKH-AS1" (0.000028)
2141. feature "SH2B3" (0.000028)
2142. feature "ATXN7L2" (0.000028)
2143. feature "RMI2" (0.000028)
2144. feature "FADS2" (0.000028)
2145. feature "ATP1A1-AS1" (0.000028)
2146. feature "SLC25A51P4" (0.000028)
2147. feature "ANKRD44" (0.000028)
2148. feature "FHL3" (0.000028)
2149. feature "FAM53C" (0.000028)
2150. feature "SCN7A" (0.000028)
2151. feature "SPHK2" (0.000028)
2152. feature "FBXO8" (0.000028)
2153. feature "PDLIM3" (0.000028)
2154. feature "ZNF182" (0.000027)
2155. feature "AMDHD2" (0.000027)
2156. feature "PSPN" (0.000027)
2157. feature "ST13P7" (0.000027)
2158. feature "TMEM241" (0.000027)
2159. feature "ADM2" (0.000027)
2160. feature "MED11" (0.000027)
2161. feature "HOXC8" (0.000027)
2162. feature "DRD4" (0.000027)
2163. feature "LINC01637" (0.000027)
2164. feature "EPOP" (0.000027)
2165. feature "ZNF469" (0.000027)
2166. feature "CCL26" (0.000027)
2167. feature "SRC" (0.000027)
2168. feature "PRPF40B" (0.000027)
2169. feature "GBP1" (0.000027)
2170. feature "TUBD1" (0.000027)
2171. feature "C19orf44" (0.000027)
2172. feature "FARS2" (0.000027)
2173. feature "TBC1D25" (0.000027)
2174. feature "MPRIP-AS1" (0.000027)
2175. feature "STON1" (0.000027)
2176. feature "HAGLROS" (0.000027)
2177. feature "HBM" (0.000027)
2178. feature "UAP1L1" (0.000027)
2179. feature "DNAH7" (0.000027)
2180. feature "NES" (0.000027)
2181. feature "NR2F2-AS1" (0.000027)
2182. feature "MTND2P28" (0.000027)
2183. feature "SIMC1" (0.000026)
2184. feature "RHOXF2B" (0.000026)
2185. feature "MYOM3" (0.000026)
2186. feature "CBWD6" (0.000026)
2187. feature "SLC25A12" (0.000026)
2188. feature "TDO2" (0.000026)
2189. feature "SLC1A2" (0.000026)
2190. feature "ARHGEF35" (0.000026)
2191. feature "CASC2" (0.000026)
2192. feature "IPPK" (0.000026)
2193. feature "CMTM3" (0.000026)
2194. feature "CYP2C9" (0.000026)
2195. feature "PDE1B" (0.000026)
2196. feature "LINC01963" (0.000026)
2197. feature "TOR2A" (0.000026)
2198. feature "KLRK1-AS1" (0.000026)
2199. feature "CLDN15" (0.000026)
2200. feature "MORN3" (0.000026)
2201. feature "LRRC3" (0.000026)
2202. feature "LINC02298" (0.000026)
2203. feature "CHST5" (0.000026)
2204. feature "MEGF6" (0.000025)
2205. feature "MSI1" (0.000025)
2206. feature "CHST14" (0.000025)
2207. feature "NUDT16L2P" (0.000025)
2208. feature "CUTC" (0.000025)
2209. feature "FAM241B" (0.000025)
2210. feature "EHD3" (0.000025)
2211. feature "HOXB3" (0.000025)
2212. feature "MT-TT" (0.000025)
2213. feature "LINC02178" (0.000025)
2214. feature "PDK2" (0.000025)
2215. feature "FSBP" (0.000025)
2216. feature "ZNF568" (0.000025)
2217. feature "AP1G2" (0.000025)
2218. feature "LINC01139" (0.000025)
2219. feature "HCP5" (0.000025)
2220. feature "PARP12" (0.000025)
2221. feature "KLHL24" (0.000025)
2222. feature "GTF2IRD1" (0.000025)
2223. feature "CUL9" (0.000025)
2224. feature "CACNB1" (0.000025)
2225. feature "ABHD12B" (0.000025)
2226. feature "UST" (0.000025)
2227. feature "WNT4" (0.000024)
2228. feature "LINC02018" (0.000024)
2229. feature "TBC1D22A-AS1" (0.000024)
2230. feature "OVOL2" (0.000024)
2231. feature "UQCRB-AS1" (0.000024)
2232. feature "ARMCX5" (0.000024)
2233. feature "PCAT14" (0.000024)
2234. feature "TMEM102" (0.000024)
2235. feature "KCNMB4" (0.000024)
2236. feature "C17orf99" (0.000024)
2237. feature "TRAF2" (0.000024)
2238. feature "SPRR3" (0.000024)
2239. feature "CDX2" (0.000024)
2240. feature "MTCO1P40" (0.000024)
2241. feature "SLC2A6" (0.000024)
2242. feature "HBA1" (0.000024)
2243. feature "PAX6" (0.000024)
2244. feature "ADO" (0.000024)
2245. feature "TMEM231" (0.000024)
2246. feature "ZNF417" (0.000024)
2247. feature "SERGEF" (0.000024)
2248. feature "ZNF416" (0.000024)
2249. feature "MT-TD" (0.000024)
2250. feature "AHCYL2" (0.000024)
2251. feature "C5AR1" (0.000024)
2252. feature "MARCHF3" (0.000024)
2253. feature "TGDS" (0.000024)
2254. feature "LINC01902" (0.000024)
2255. feature "BOK" (0.000024)
2256. feature "PRKAA2" (0.000024)
2257. feature "CACNB2" (0.000024)
2258. feature "TUBB3" (0.000024)
2259. feature "CAB39L" (0.000023)
2260. feature "ZNF433" (0.000023)
2261. feature "CAVIN2" (0.000023)
2262. feature "PHF1" (0.000023)
2263. feature "HERPUD2-AS1" (0.000023)
2264. feature "ESRRA" (0.000023)
2265. feature "MAU2" (0.000023)
2266. feature "NDN" (0.000023)
2267. feature "ZSCAN9" (0.000023)
2268. feature "FAM110C" (0.000023)
2269. feature "FAM166C" (0.000023)
2270. feature "FCRL5" (0.000023)
2271. feature "PPFIA3" (0.000023)
2272. feature "MT1F" (0.000023)
2273. feature "ARMC5" (0.000023)
2274. feature "ARL14" (0.000023)
2275. feature "NXT2" (0.000023)
2276. feature "DMD" (0.000023)
2277. feature "NFKBID" (0.000023)
2278. feature "LINC01546" (0.000023)
2279. feature "SYCE1L" (0.000023)
2280. feature "DUSP9" (0.000023)
2281. feature "CFAP410" (0.000022)
2282. feature "CPB1" (0.000022)
2283. feature "ZBED8" (0.000022)
2284. feature "RNF215" (0.000022)
2285. feature "CCDC97" (0.000022)
2286. feature "ABO" (0.000022)
2287. feature "USP30" (0.000022)
2288. feature "PPP2R3B" (0.000022)
2289. feature "PALM" (0.000022)
2290. feature "FAM131A" (0.000022)
2291. feature "TMTC4" (0.000022)
2292. feature "UBAC2" (0.000022)
2293. feature "ZNF747" (0.000022)
2294. feature "DOCK8-AS1" (0.000022)
2295. feature "ZNF418" (0.000022)
2296. feature "PLD1" (0.000022)
2297. feature "PLEKHG6" (0.000022)
2298. feature "MEX3A" (0.000022)
2299. feature "LRRC23" (0.000022)
2300. feature "ADGRE1" (0.000022)
2301. feature "SNX33" (0.000022)
2302. feature "TRANK1" (0.000022)
2303. feature "ROBO1" (0.000022)
2304. feature "LINC01679" (0.000022)
2305. feature "CUEDC1" (0.000022)
2306. feature "VASH2" (0.000022)
2307. feature "MOSPD3" (0.000022)
2308. feature "ADA2" (0.000022)
2309. feature "MB" (0.000022)
2310. feature "SNORA66" (0.000022)
2311. feature "MFAP3L" (0.000022)
2312. feature "DENND2D" (0.000022)
2313. feature "RDH14" (0.000021)
2314. feature "TRG-AS1" (0.000021)
2315. feature "POMK" (0.000021)
2316. feature "MORN1" (0.000021)
2317. feature "FGF22" (0.000021)
2318. feature "ZNF341" (0.000021)
2319. feature "NANOS1" (0.000021)
2320. feature "FOXF2" (0.000021)
2321. feature "RAPGEF6" (0.000021)
2322. feature "LASTR" (0.000021)
2323. feature "FTHL17" (0.000021)
2324. feature "ZNF263" (0.000021)
2325. feature "PRRX2" (0.000021)
2326. feature "YJEFN3" (0.000021)
2327. feature "HOXA-AS2" (0.000021)
2328. feature "PIGCP1" (0.000021)
2329. feature "CCDC15-DT" (0.000021)
2330. feature "LRTM1" (0.000021)
2331. feature "AQP3" (0.000021)
2332. feature "PBX4" (0.000021)
2333. feature "ITGBL1" (0.000021)
2334. feature "KAT2B" (0.000021)
2335. feature "MAP4K1" (0.000021)
2336. feature "IL15RA" (0.000021)
2337. feature "HDDC3" (0.000021)
2338. feature "SPDYE3" (0.000021)
2339. feature "ABTB2" (0.000021)
2340. feature "SPART-AS1" (0.000021)
2341. feature "MT1A" (0.000021)
2342. feature "SLC41A3" (0.000021)
2343. feature "LINC01666" (0.000021)
2344. feature "SEPTIN1" (0.000021)
2345. feature "FAM86C2P" (0.000021)
2346. feature "TBC1D10A" (0.000021)
2347. feature "GRK4" (0.000021)
2348. feature "PAQR9-AS1" (0.000021)
2349. feature "SPRY1" (0.000021)
2350. feature "SCRN2" (0.000020)
2351. feature "RAB30" (0.000020)
2352. feature "GPRIN2" (0.000020)
2353. feature "CLHC1" (0.000020)
2354. feature "C19orf81" (0.000020)
2355. feature "EPHX2" (0.000020)
2356. feature "DLGAP1-AS2" (0.000020)
2357. feature "KBTBD4" (0.000020)
2358. feature "AGAP1-IT1" (0.000020)
2359. feature "PCOLCE" (0.000020)
2360. feature "CORO7" (0.000020)
2361. feature "HSPD1P11" (0.000020)
2362. feature "TCAIM" (0.000020)
2363. feature "MT-TM" (0.000020)
2364. feature "AXIN2" (0.000020)
2365. feature "SH3BP5L" (0.000020)
2366. feature "TBC1D19" (0.000020)
2367. feature "PRRG2" (0.000020)
2368. feature "ZNF169" (0.000020)
2369. feature "NLRP10" (0.000020)
2370. feature "MSTO1" (0.000020)
2371. feature "ZNF879" (0.000020)
2372. feature "DPYSL3" (0.000020)
2373. feature "PBLD" (0.000020)
2374. feature "ZKSCAN4" (0.000020)
2375. feature "TMCO6" (0.000020)
2376. feature "RPS6KL1" (0.000020)
2377. feature "LRP4" (0.000020)
2378. feature "PITPNC1" (0.000020)
2379. feature "CBLL1-AS1" (0.000020)
2380. feature "LBHD2" (0.000020)
2381. feature "RAB11FIP4" (0.000020)
2382. feature "COL1A1" (0.000019)
2383. feature "S100P" (0.000019)
2384. feature "HLA-V" (0.000019)
2385. feature "PROS1" (0.000019)
2386. feature "TRAPPC6A" (0.000019)
2387. feature "CYB561D1" (0.000019)
2388. feature "IL21R" (0.000019)
2389. feature "UPF3AP2" (0.000019)
2390. feature "P2RX7" (0.000019)
2391. feature "RHOXF2" (0.000019)
2392. feature "RSAD1" (0.000019)
2393. feature "YPEL2" (0.000019)
2394. feature "DANT2" (0.000019)
2395. feature "TIGD2" (0.000019)
2396. feature "MICB" (0.000019)
2397. feature "RCCD1" (0.000019)
2398. feature "PGBD2" (0.000019)
2399. feature "MYO18B" (0.000019)
2400. feature "NPIPB4" (0.000019)
2401. feature "SYNM" (0.000019)
2402. feature "WDR35" (0.000019)
2403. feature "IFI35" (0.000019)
2404. feature "TINF2" (0.000018)
2405. feature "CLCF1" (0.000018)
2406. feature "LRCH4" (0.000018)
2407. feature "CRACDL" (0.000018)
2408. feature "USF1" (0.000018)
2409. feature "ACVR1C" (0.000018)
2410. feature "GLIPR1" (0.000018)
2411. feature "LPAR5" (0.000018)
2412. feature "ARHGEF17" (0.000018)
2413. feature "CNPY2-AS1" (0.000018)
2414. feature "ZNF90P3" (0.000018)
2415. feature "YBEY" (0.000018)
2416. feature "RPL5P3" (0.000018)
2417. feature "NKX2-8" (0.000018)
2418. feature "MAP3K10" (0.000018)
2419. feature "HS1BP3" (0.000018)
2420. feature "LINC02273" (0.000018)
2421. feature "LSP1P4" (0.000018)
2422. feature "C2orf27A" (0.000018)
2423. feature "SNX11" (0.000018)
2424. feature "ADAM19" (0.000018)
2425. feature "IRAK3" (0.000018)
2426. feature "OR2A5" (0.000018)
2427. feature "NPEPL1" (0.000018)
2428. feature "HDAC5" (0.000018)
2429. feature "LINC01843" (0.000018)
2430. feature "DISP1" (0.000018)
2431. feature "DAB2IP" (0.000018)
2432. feature "CCDC40" (0.000018)
2433. feature "ENKD1" (0.000018)
2434. feature "SLC31A2" (0.000018)
2435. feature "KATNB1" (0.000018)
2436. feature "MIR137HG" (0.000018)
2437. feature "BCKDHB" (0.000018)
2438. feature "RAB15" (0.000018)
2439. feature "LINC01087" (0.000017)
2440. feature "DYNLT2" (0.000017)
2441. feature "ZNF256" (0.000017)
2442. feature "SORBS1" (0.000017)
2443. feature "NEK8" (0.000017)
2444. feature "LRRC20" (0.000017)
2445. feature "GREP1" (0.000017)
2446. feature "MUC4" (0.000017)
2447. feature "GOLGA6L2" (0.000017)
2448. feature "MIR663B" (0.000017)
2449. feature "FAM81A" (0.000017)
2450. feature "HSP90AA4P" (0.000017)
2451. feature "MIOS-DT" (0.000017)
2452. feature "ALX1" (0.000017)
2453. feature "C16orf95" (0.000017)
2454. feature "LHX6" (0.000017)
2455. feature "OSER1-DT" (0.000017)
2456. feature "MBTPS1-DT" (0.000017)
2457. feature "DPPA4" (0.000017)
2458. feature "TICAM1" (0.000017)
2459. feature "LINC02672" (0.000017)
2460. feature "ROGDI" (0.000017)
2461. feature "PECAM1" (0.000017)
2462. feature "SLC35A2" (0.000017)
2463. feature "ALDH6A1" (0.000017)
2464. feature "CLMN" (0.000017)
2465. feature "SLC2A3" (0.000017)
2466. feature "CXCR2" (0.000017)
2467. feature "FZD8" (0.000017)
2468. feature "TBC1D22A-DT" (0.000017)
2469. feature "MAP7D2" (0.000017)
2470. feature "NOMO3" (0.000017)
2471. feature "ADPRM" (0.000017)
2472. feature "SNN" (0.000017)
2473. feature "STK36" (0.000017)
2474. feature "C1QL1" (0.000017)
2475. feature "CCDC191" (0.000017)
2476. feature "RPL24P9" (0.000017)
2477. feature "HEIH" (0.000016)
2478. feature "ZNF782" (0.000016)
2479. feature "TMEM125" (0.000016)
2480. feature "FGD1" (0.000016)
2481. feature "ZNF212" (0.000016)
2482. feature "MEF2D" (0.000016)
2483. feature "RADIL" (0.000016)
2484. feature "FBXO43" (0.000016)
2485. feature "TMEM144" (0.000016)
2486. feature "ABHD14B" (0.000016)
2487. feature "EEPD1" (0.000016)
2488. feature "CSPG4P12" (0.000016)
2489. feature "RINL" (0.000016)
2490. feature "ZNF826P" (0.000016)
2491. feature "ANO7L1" (0.000016)
2492. feature "PKHD1L1" (0.000016)
2493. feature "SPATA6L" (0.000016)
2494. feature "NLRC5" (0.000016)
2495. feature "LRRC75B" (0.000016)
2496. feature "IL1A" (0.000016)
2497. feature "LMX1B" (0.000016)
2498. feature "COX4I2" (0.000016)
2499. feature "HMGN1P17" (0.000016)
2500. feature "LY6G5C" (0.000016)
2501. feature "PRR36" (0.000016)
2502. feature "SMCO4" (0.000016)
2503. feature "FBXL16" (0.000016)
2504. feature "HPCA" (0.000016)
2505. feature "LYPD6" (0.000016)
2506. feature "TLE2" (0.000016)
2507. feature "PFKFB4" (0.000015)
2508. feature "EVC2" (0.000015)
2509. feature "MAILR" (0.000015)
2510. feature "C21orf62-AS1" (0.000015)
2511. feature "GASAL1" (0.000015)
2512. feature "CNEP1R1" (0.000015)
2513. feature "BMP4" (0.000015)
2514. feature "FSIP1" (0.000015)
2515. feature "ARTN" (0.000015)
2516. feature "OSCP1" (0.000015)
2517. feature "PFKFB2" (0.000015)
2518. feature "IKBKE" (0.000015)
2519. feature "RNF166" (0.000015)
2520. feature "ITGA11" (0.000015)
2521. feature "OR10K1" (0.000015)
2522. feature "LINC00320" (0.000015)
2523. feature "GBP2" (0.000015)
2524. feature "LINC02894" (0.000015)
2525. feature "C11orf98" (0.000015)
2526. feature "EEF1A1P25" (0.000015)
2527. feature "GRAMD1B" (0.000015)
2528. feature "CTIF" (0.000015)
2529. feature "MS4A10" (0.000015)
2530. feature "KCNMA1" (0.000015)
2531. feature "SERPINF1" (0.000015)
2532. feature "EFCAB1" (0.000015)
2533. feature "TYMSOS" (0.000015)
2534. feature "NFKBIE" (0.000015)
2535. feature "NKX2-1" (0.000015)
2536. feature "KIF21B" (0.000015)
2537. feature "FLNC" (0.000015)
2538. feature "LINC02664" (0.000015)
2539. feature "SH3GL3" (0.000015)
2540. feature "LINC01102" (0.000015)
2541. feature "OXSM" (0.000015)
2542. feature "NR1D1" (0.000014)
2543. feature "DUOX1" (0.000014)
2544. feature "CLDND2" (0.000014)
2545. feature "BDH2" (0.000014)
2546. feature "FNBP1P1" (0.000014)
2547. feature "BMI1" (0.000014)
2548. feature "PSMD10P2" (0.000014)
2549. feature "ADGRF4" (0.000014)
2550. feature "SLC38A4" (0.000014)
2551. feature "RPL9P32" (0.000014)
2552. feature "NOP14-AS1" (0.000014)
2553. feature "AMZ1" (0.000014)
2554. feature "LINC01266" (0.000014)
2555. feature "TRAM1L1" (0.000014)
2556. feature "PHC1" (0.000014)
2557. feature "LINC01607" (0.000014)
2558. feature "MYO1F" (0.000014)
2559. feature "ZSCAN18" (0.000014)
2560. feature "LINC01460" (0.000014)
2561. feature "LTBP1" (0.000014)
2562. feature "HAGLR" (0.000014)
2563. feature "PLA2G6" (0.000014)
2564. feature "HDAC11" (0.000014)
2565. feature "H2AW" (0.000014)
2566. feature "LTBP2" (0.000014)
2567. feature "SIGLEC10" (0.000014)
2568. feature "ACTR1B" (0.000014)
2569. feature "MAP3K2-DT" (0.000014)
2570. feature "PROC" (0.000014)
2571. feature "CAPNS2" (0.000014)
2572. feature "NECTIN4" (0.000014)
2573. feature "LTB4R" (0.000014)
2574. feature "PSKH1" (0.000014)
2575. feature "HECW2" (0.000014)
2576. feature "INTS6-AS1" (0.000014)
2577. feature "ATL1" (0.000014)
2578. feature "ANXA6" (0.000014)
2579. feature "FOXD1" (0.000014)
2580. feature "NLGN2" (0.000014)
2581. feature "TUBA1A" (0.000013)
2582. feature "B4GALNT2" (0.000013)
2583. feature "ACVR1" (0.000013)
2584. feature "FAM111A-DT" (0.000013)
2585. feature "YWHAEP5" (0.000013)
2586. feature "BATF3" (0.000013)
2587. feature "RGL3" (0.000013)
2588. feature "ASCL2" (0.000013)
2589. feature "TPRG1" (0.000013)
2590. feature "RNF32" (0.000013)
2591. feature "IFITM10" (0.000013)
2592. feature "MAN1A1" (0.000013)
2593. feature "CDH24" (0.000013)
2594. feature "TRIM45" (0.000013)
2595. feature "DNAJC3-DT" (0.000013)
2596. feature "RN7SL471P" (0.000013)
2597. feature "MATN2" (0.000013)
2598. feature "RAB9A" (0.000013)
2599. feature "TRDN-AS1" (0.000013)
2600. feature "ZBTB47" (0.000013)
2601. feature "LBX2" (0.000013)
2602. feature "NSA2P4" (0.000013)
2603. feature "TGM2" (0.000013)
2604. feature "CFAP45" (0.000013)
2605. feature "MESP1" (0.000013)
2606. feature "GAB1" (0.000013)
2607. feature "RRAGD" (0.000013)
2608. feature "FZD1" (0.000012)
2609. feature "NLRP8" (0.000012)
2610. feature "FAM43A" (0.000012)
2611. feature "TM7SF2" (0.000012)
2612. feature "SPX" (0.000012)
2613. feature "RCBTB2" (0.000012)
2614. feature "COL19A1" (0.000012)
2615. feature "ABCA7" (0.000012)
2616. feature "IQCH-AS1" (0.000012)
2617. feature "LIPE-AS1" (0.000012)
2618. feature "IL11" (0.000012)
2619. feature "WFDC3" (0.000012)
2620. feature "ARID5A" (0.000012)
2621. feature "CTBS" (0.000012)
2622. feature "RCCD1-AS1" (0.000012)
2623. feature "ANKMY1" (0.000012)
2624. feature "ADCK1" (0.000012)
2625. feature "STARD13-AS" (0.000012)
2626. feature "NUAK2" (0.000012)
2627. feature "TMEM79" (0.000012)
2628. feature "CCDC141" (0.000012)
2629. feature "HHLA2" (0.000012)
2630. feature "CNOT6LP1" (0.000012)
2631. feature "PLAAT4" (0.000012)
2632. feature "LINC01816" (0.000012)
2633. feature "CSAG3" (0.000012)
2634. feature "RPL12P42" (0.000012)
2635. feature "NTF4" (0.000012)
2636. feature "MSRB3" (0.000012)
2637. feature "CCPG1" (0.000012)
2638. feature "RPL23AP7" (0.000012)
2639. feature "H1-6" (0.000012)
2640. feature "ACBD4" (0.000012)
2641. feature "DALRD3" (0.000012)
2642. feature "LAX1" (0.000012)
2643. feature "MT-TF" (0.000012)
2644. feature "AKAP6" (0.000012)
2645. feature "TTC30A" (0.000012)
2646. feature "CES1" (0.000012)
2647. feature "LONRF1" (0.000012)
2648. feature "HOXA7" (0.000012)
2649. feature "TNC" (0.000012)
2650. feature "ROR2" (0.000012)
2651. feature "LRRC2" (0.000011)
2652. feature "GPRC5D" (0.000011)
2653. feature "MIR194-2HG" (0.000011)
2654. feature "ZNF99" (0.000011)
2655. feature "ACSF2" (0.000011)
2656. feature "LINC00911" (0.000011)
2657. feature "TTLL1" (0.000011)
2658. feature "YBX1P6" (0.000011)
2659. feature "TMEM128" (0.000011)
2660. feature "PSD3" (0.000011)
2661. feature "BMP2" (0.000011)
2662. feature "DGCR6" (0.000011)
2663. feature "ZNF76" (0.000011)
2664. feature "AQP11" (0.000011)
2665. feature "FOXO4" (0.000011)
2666. feature "ST3GAL6" (0.000011)
2667. feature "REP15" (0.000011)
2668. feature "PANK1" (0.000011)
2669. feature "LINC01615" (0.000011)
2670. feature "FUT2" (0.000011)
2671. feature "XKR8" (0.000011)
2672. feature "LINC02541" (0.000011)
2673. feature "GHDC" (0.000011)
2674. feature "LINC02029" (0.000011)
2675. feature "AMBRA1" (0.000011)
2676. feature "ELOVL7" (0.000011)
2677. feature "NAPA-AS1" (0.000011)
2678. feature "LINC02615" (0.000011)
2679. feature "LCAT" (0.000011)
2680. feature "CARF" (0.000011)
2681. feature "ARL15" (0.000011)
2682. feature "CABP4" (0.000011)
2683. feature "CMYA5" (0.000011)
2684. feature "PWWP2B" (0.000010)
2685. feature "DMRTA2" (0.000010)
2686. feature "MT-TI" (0.000010)
2687. feature "METAP1D" (0.000010)
2688. feature "CTHRC1" (0.000010)
2689. feature "HOXC6" (0.000010)
2690. feature "CTF1" (0.000010)
2691. feature "IL10RB" (0.000010)
2692. feature "NOVA2" (0.000010)
2693. feature "TLR2" (0.000010)
2694. feature "MPG" (0.000010)
2695. feature "FTH1P16" (0.000010)
2696. feature "MAN1C1" (0.000010)
2697. feature "NDST2" (0.000010)
2698. feature "OR10D3" (0.000010)
2699. feature "CYP2J2" (0.000010)
2700. feature "HSD11B2" (0.000010)
2701. feature "ZNF571-AS1" (0.000010)
2702. feature "ZNRF2" (0.000010)
2703. feature "LINC02245" (0.000010)
2704. feature "FDXACB1" (0.000010)
2705. feature "HSPD1P5" (0.000010)
2706. feature "CCDC9B" (0.000010)
2707. feature "ARHGEF10L" (0.000010)
2708. feature "LINC01667" (0.000010)
2709. feature "WNT7A" (0.000010)
2710. feature "MUS81" (0.000010)
2711. feature "NTRK2" (0.000010)
2712. feature "SLC2A10" (0.000010)
2713. feature "UNC13C" (0.000010)
2714. feature "LINC01764" (0.000010)
2715. feature "ST3GAL1-DT" (0.000010)
2716. feature "USP20" (0.000010)
2717. feature "POTEC" (0.000010)
2718. feature "SRP14-DT" (0.000009)
2719. feature "PARD6A" (0.000009)
2720. feature "DOC2A" (0.000009)
2721. feature "C3orf35" (0.000009)
2722. feature "OCEL1" (0.000009)
2723. feature "KMT2E-AS1" (0.000009)
2724. feature "CCR12P" (0.000009)
2725. feature "SMCR5" (0.000009)
2726. feature "ABCC13" (0.000009)
2727. feature "SLAMF9" (0.000009)
2728. feature "FGFBP3" (0.000009)
2729. feature "PARP16" (0.000009)
2730. feature "ROBO3" (0.000009)
2731. feature "SLC6A9" (0.000009)
2732. feature "MAP3K8" (0.000009)
2733. feature "ZFYVE1" (0.000009)
2734. feature "SLC30A6-DT" (0.000009)
2735. feature "TRAIP" (0.000009)
2736. feature "ARMCX6" (0.000009)
2737. feature "ARMCX4" (0.000009)
2738. feature "N4BP2L2-IT2" (0.000009)
2739. feature "TMEM182" (0.000009)
2740. feature "ST20" (0.000009)
2741. feature "SAMD10" (0.000009)
2742. feature "GAREM2" (0.000009)
2743. feature "SERPINB10" (0.000009)
2744. feature "SDK1" (0.000009)
2745. feature "PGLS-DT" (0.000009)
2746. feature "RIMKLA" (0.000009)
2747. feature "KIAA2012-AS1" (0.000009)
2748. feature "LINC01415" (0.000009)
2749. feature "ATP8B2" (0.000009)
2750. feature "LRFN1" (0.000009)
2751. feature "NRXN2" (0.000009)
2752. feature "FOXE1" (0.000009)
2753. feature "MT1G" (0.000009)
2754. feature "GAS7" (0.000009)
2755. feature "LINC01533" (0.000009)
2756. feature "WDR24" (0.000008)
2757. feature "SNURF" (0.000008)
2758. feature "FER1L4" (0.000008)
2759. feature "CCDC153" (0.000008)
2760. feature "LINC01091" (0.000008)
2761. feature "LINC00683" (0.000008)
2762. feature "CATSPERB" (0.000008)
2763. feature "CAMK2B" (0.000008)
2764. feature "UFSP1" (0.000008)
2765. feature "PCYOX1L" (0.000008)
2766. feature "CD2AP-DT" (0.000008)
2767. feature "FAM166A" (0.000008)
2768. feature "NGEF" (0.000008)
2769. feature "GMIP" (0.000008)
2770. feature "SDSL" (0.000008)
2771. feature "SMO" (0.000008)
2772. feature "GLB1L" (0.000008)
2773. feature "ZNF132-DT" (0.000008)
2774. feature "ITGA1" (0.000008)
2775. feature "POTEF" (0.000008)
2776. feature "ABTB1" (0.000008)
2777. feature "NAALAD2" (0.000008)
2778. feature "SPATA2L" (0.000008)
2779. feature "GNGT1" (0.000008)
2780. feature "PTPRB" (0.000008)
2781. feature "CASC9" (0.000008)
2782. feature "SMOX" (0.000008)
2783. feature "TCL6" (0.000008)
2784. feature "RELB" (0.000008)
2785. feature "TNFRSF11A" (0.000008)
2786. feature "MTMR11" (0.000007)
2787. feature "ZNF577" (0.000007)
2788. feature "TUSC1" (0.000007)
2789. feature "RHOQP3" (0.000007)
2790. feature "ACADS" (0.000007)
2791. feature "TAF6L" (0.000007)
2792. feature "CRTAM" (0.000007)
2793. feature "CDK6-AS1" (0.000007)
2794. feature "C12orf50" (0.000007)
2795. feature "SLC2A8" (0.000007)
2796. feature "APOL3" (0.000007)
2797. feature "SPATA2" (0.000007)
2798. feature "LIMD2" (0.000007)
2799. feature "LARGE1" (0.000007)
2800. feature "RELN" (0.000007)
2801. feature "NUP62CL" (0.000007)
2802. feature "KANK3" (0.000007)
2803. feature "BMP8A" (0.000007)
2804. feature "ZBTB22" (0.000007)
2805. feature "TRPT1" (0.000007)
2806. feature "RASGRP2" (0.000007)
2807. feature "MIR320A" (0.000007)
2808. feature "PARS2" (0.000007)
2809. feature "ZNF517" (0.000007)
2810. feature "SLC9B1" (0.000007)
2811. feature "MT-TH" (0.000007)
2812. feature "MAD1L1" (0.000007)
2813. feature "STAG3L5P-PVRIG2P-PILRB" (0.000007)
2814. feature "STARD7-AS1" (0.000007)
2815. feature "KRBOX4" (0.000007)
2816. feature "MSH5" (0.000007)
2817. feature "TNFRSF11B" (0.000006)
2818. feature "SERTAD4-AS1" (0.000006)
2819. feature "KIAA2012" (0.000006)
2820. feature "NOTCH2NLC" (0.000006)
2821. feature "ART3" (0.000006)
2822. feature "CA11" (0.000006)
2823. feature "CHMP1B2P" (0.000006)
2824. feature "DET1" (0.000006)
2825. feature "ACP5" (0.000006)
2826. feature "TMEM158" (0.000006)
2827. feature "SREK1IP1P1" (0.000006)
2828. feature "ADTRP" (0.000006)
2829. feature "NARF-AS2" (0.000006)
2830. feature "LINC01772" (0.000006)
2831. feature "NOC2LP1" (0.000006)
2832. feature "MCMDC2" (0.000006)
2833. feature "LINC01560" (0.000006)
2834. feature "HPS6" (0.000006)
2835. feature "LINC01551" (0.000006)
2836. feature "BLOC1S6P1" (0.000006)
2837. feature "ATP1B2" (0.000006)
2838. feature "PHC2" (0.000006)
2839. feature "ZBTB46" (0.000006)
2840. feature "THBS1-IT1" (0.000006)
2841. feature "RPS12P16" (0.000006)
2842. feature "YWHAQP5" (0.000006)
2843. feature "LERFS" (0.000006)
2844. feature "MT-TK" (0.000006)
2845. feature "TK2" (0.000006)
2846. feature "CARD9" (0.000006)
2847. feature "DHX35-DT" (0.000006)
2848. feature "LCTL" (0.000006)
2849. feature "ANOS1" (0.000005)
2850. feature "FAT4" (0.000005)
2851. feature "PADI2" (0.000005)
2852. feature "LINC02899" (0.000005)
2853. feature "UPRT" (0.000005)
2854. feature "POLM" (0.000005)
2855. feature "PDE9A" (0.000005)
2856. feature "LINC01405" (0.000005)
2857. feature "GPR160" (0.000005)
2858. feature "BBS4" (0.000005)
2859. feature "ZNF25" (0.000005)
2860. feature "GVINP1" (0.000005)
2861. feature "SYT15" (0.000005)
2862. feature "SMC1B" (0.000005)
2863. feature "SLC9A3" (0.000005)
2864. feature "STIM2-AS1" (0.000005)
2865. feature "NUPR2" (0.000005)
2866. feature "LZTR1" (0.000005)
2867. feature "FABP3" (0.000005)
2868. feature "HHAT" (0.000005)
2869. feature "APLF" (0.000005)
2870. feature "FRAT2" (0.000004)
2871. feature "FAM131C" (0.000004)
2872. feature "DTNB" (0.000004)
2873. feature "C11orf71" (0.000004)
2874. feature "PRORSD1P" (0.000004)
2875. feature "RIMS2" (0.000004)
2876. feature "RSPH1" (0.000004)
2877. feature "ZP3" (0.000004)
2878. feature "MPPE1" (0.000004)
2879. feature "SAMMSON" (0.000004)
2880. feature "CRY2" (0.000004)
2881. feature "LRRC26" (0.000004)
2882. feature "UNC5B-AS1" (0.000004)
2883. feature "DUT-AS1" (0.000004)
2884. feature "ZBTB18" (0.000004)
2885. feature "TRNP1" (0.000004)
2886. feature "PADI1" (0.000004)
2887. feature "LINC00471" (0.000004)
2888. feature "RFTN1" (0.000004)
2889. feature "RPS18P9" (0.000004)
2890. feature "CREB3L1" (0.000004)
2891. feature "ANO8" (0.000004)
2892. feature "ASB2" (0.000004)
2893. feature "SLITRK2" (0.000004)
2894. feature "C14orf132" (0.000004)
2895. feature "CNDP1" (0.000004)
2896. feature "TNFRSF14" (0.000004)
2897. feature "NAP1L1P1" (0.000004)
2898. feature "KSR1" (0.000004)
2899. feature "MYH7B" (0.000004)
2900. feature "ITGA4" (0.000004)
2901. feature "TSNARE1" (0.000004)
2902. feature "MMP28" (0.000003)
2903. feature "LINC01840" (0.000003)
2904. feature "ANG" (0.000003)
2905. feature "FIBCD1" (0.000003)
2906. feature "CDHR3" (0.000003)
2907. feature "UQCRHL" (0.000003)
2908. feature "RPL34P1" (0.000003)
2909. feature "SLC8A1" (0.000003)
2910. feature "DENND4B" (0.000003)
2911. feature "GOLGA2P5" (0.000003)
2912. feature "FA2H" (0.000003)
2913. feature "NEURL1" (0.000003)
2914. feature "CCDC61" (0.000003)
2915. feature "CCDC62" (0.000003)
2916. feature "LGR6" (0.000003)
2917. feature "LOX" (0.000003)
2918. feature "MIF4GD-DT" (0.000003)
2919. feature "LENG9" (0.000003)
2920. feature "RNF123" (0.000003)
2921. feature "SMPDL3A" (0.000003)
2922. feature "MAP1LC3B2" (0.000003)
2923. feature "SHF" (0.000003)
2924. feature "LINC01854" (0.000003)
2925. feature "C12orf76" (0.000003)
2926. feature "PPIEL" (0.000003)
2927. feature "C17orf100" (0.000002)
2928. feature "PCDHB11" (0.000002)
2929. feature "ARHGEF33" (0.000002)
2930. feature "POTEM" (0.000002)
2931. feature "MYH15" (0.000002)
2932. feature "CRB3" (0.000002)
2933. feature "WWOX-AS1" (0.000002)
2934. feature "SUSD5" (0.000002)
2935. feature "GIHCG" (0.000002)
2936. feature "FBXO33" (0.000002)
2937. feature "STC1" (0.000002)
2938. feature "DYNC1LI2-DT" (0.000002)
2939. feature "PIM2" (0.000002)
2940. feature "ZNF682" (0.000002)
2941. feature "RNF227" (0.000002)
2942. feature "OR52A1" (0.000002)
2943. feature "NEK10" (0.000002)
2944. feature "TAF1C" (0.000002)
2945. feature "REEP1" (0.000002)
2946. feature "INTS9" (0.000002)
2947. feature "PPIAP46" (0.000001)
2948. feature "CTU2" (0.000001)
2949. feature "PPFIA4" (0.000001)
2950. feature "PPP2R5B" (0.000001)
2951. feature "ZNF687-AS1" (0.000001)
2952. feature "PAN2" (0.000001)
2953. feature "RPS15AP29" (0.000001)
2954. feature "HOXA4" (0.000001)
2955. feature "BMP8B" (0.000001)
2956. feature "CDKN1C" (0.000000)
2957. feature "SLC30A10" (0.000000)
2958. feature "LINC01695" (0.000000)
2959. feature "SUNO1" (0.000000)
2960. feature "QPCTL" (0.000000)
2961. feature "OR4K17" (0.000000)
2962. feature "BBS12" (0.000000)
2963. feature "NLRP2B" (0.000000)
2964. feature "FOXO3B" (0.000000)
2965. feature "SULT2A1" (0.000000)
2966. feature "NAT1" (0.000000)
2967. feature "ACTBP2" (0.000000)
2968. feature "COL9A2" (0.000000)
2969. feature "NOD1" (0.000000)
2970. feature "C4orf46" (0.000000)
2971. feature "SYNPO2L" (0.000000)
2972. feature "LRRC61" (0.000000)
2973. feature "LINC00663" (0.000000)
2974. feature "EDAR" (0.000000)
2975. feature "ZCRB1P1" (0.000000)
2976. feature "RAB42" (0.000000)
2977. feature "ITPRIP" (0.000000)
2978. feature "ZNF837" (0.000000)
2979. feature "ODAD2" (0.000000)
2980. feature "TRPM2-AS" (0.000000)
2981. feature "HOXB2" (0.000000)
2982. feature "TP53INP2" (0.000000)
2983. feature "H3C12" (0.000000)
2984. feature "CABLES1" (0.000000)
2985. feature "FLACC1" (0.000000)
2986. feature "NRSN1" (0.000000)
2987. feature "TLX3" (0.000000)
2988. feature "INCA1" (0.000000)
2989. feature "NUP153-AS1" (0.000000)
2990. feature "ZDHHC1" (0.000000)
2991. feature "TMPOP2" (0.000000)
2992. feature "ZFYVE28" (0.000000)
2993. feature "THSD7A" (0.000000)
2994. feature "ECHDC3" (0.000000)
2995. feature "TMCC2" (0.000000)
2996. feature "NUDT18" (0.000000)
2997. feature "LINC01687" (0.000000)
2998. feature "RAB17" (0.000000)
2999. feature "WWTR1-AS1" (0.000000)
3000. feature "FAM214B" (0.000000)
¶
4.4.3) XGBoost
Also in this case we tried to see if the Xgboost predictor could beat the performance of the Random forest classifier.
Indeed it happened, we built the model as the previous one, the parameters in the grid search are the same as the Random forest, only the max depth was decreased.
It achieves a performance of 94,91%, that up to us is an acceptable score as this dataset seems the most difficult to make predictions.
#i want to build an XGBoost classifier for the Dropseq HCC1806 dataset where random forest classifier has the worst performance
# Define the XGBoost classifier
xgb = XGBClassifier(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [100, 150], 'max_depth': [30, 50, 60]}
grid_search = GridSearchCV(xgb, param_grid, cv=5)
outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)
# Perform the cross validation
scores = cross_val_score(grid_search, X_train_df_DS_HCC1806, y_train_df_DS_HCC1806, cv=outer_cv)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))
# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_DS_HCC1806, y_train_df_DS_HCC1806)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_DS_HCC1806, y_test_df_DS_HCC1806))
# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_DS_HCC1806.shape[1]):
print("%d. feature %s (%f)" % (f + 1, X_train_df_DS_HCC1806.columns[indices[f]], importances[indices[f]]))
best_xgb_DS_HCC1806 = grid_search.best_estimator_
Mean accuracy of nested cross validation: 0.9436603803951641
Best hyperparameters: {'max_depth': 30, 'n_estimators': 150}
Best accuracy: 0.9441475191002301
Best model: XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=30, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=150, n_jobs=None,
num_parallel_tree=None, random_state=42, ...)
Test accuracy: 0.9491486946651533
Feature ranking:
1. feature "NDRG1" (0.070036)
2. feature "AKR1C2" (0.017882)
3. feature "FGF23" (0.015801)
4. feature "IGFBP3" (0.014589)
5. feature "EGLN3" (0.014030)
6. feature "AKR1C1" (0.010664)
7. feature "PLEKHF1" (0.008650)
8. feature "ECH1" (0.008633)
9. feature "WDR43" (0.008219)
10. feature "RNF20" (0.008062)
11. feature "BNIP3" (0.007771)
12. feature "RGPD4-AS1" (0.007295)
13. feature "SART1" (0.007176)
14. feature "GPM6A" (0.006632)
15. feature "RNF122" (0.006158)
16. feature "LDHA" (0.006072)
17. feature "PUSL1" (0.005928)
18. feature "P4HA1" (0.005924)
19. feature "VIT" (0.005616)
20. feature "INO80E" (0.005482)
21. feature "CCNG2" (0.005472)
22. feature "MT-TV" (0.005431)
23. feature "ANGPTL4" (0.005312)
24. feature "EIF3J" (0.005251)
25. feature "AXL" (0.004537)
26. feature "AMOTL1" (0.004435)
27. feature "POLR2J" (0.004342)
28. feature "LUZP1" (0.004263)
29. feature "RPS26" (0.004192)
30. feature "FPGS" (0.004055)
31. feature "FOSL1" (0.003959)
32. feature "MT-ATP8" (0.003931)
33. feature "BTBD9" (0.003924)
34. feature "HS6ST1" (0.003752)
35. feature "RHOD" (0.003671)
36. feature "KMT2D" (0.003664)
37. feature "BCYRN1" (0.003604)
38. feature "HSPD1" (0.003604)
39. feature "FNDC10" (0.003538)
40. feature "TMSB10" (0.003503)
41. feature "BNIP3L" (0.003485)
42. feature "YKT6" (0.003472)
43. feature "ITSN2" (0.003423)
44. feature "COX8A" (0.003421)
45. feature "ARHGDIA" (0.003357)
46. feature "TCF7L1" (0.003353)
47. feature "COX14" (0.003336)
48. feature "NPM1P40" (0.003281)
49. feature "MT-CYB" (0.003264)
50. feature "RFK" (0.003206)
51. feature "PGBD1" (0.003154)
52. feature "NEUROD1" (0.003069)
53. feature "STC2" (0.003052)
54. feature "DDIT4" (0.003027)
55. feature "NEAT1" (0.002973)
56. feature "PGK1" (0.002968)
57. feature "DIO2" (0.002935)
58. feature "CAVIN3" (0.002899)
59. feature "OIP5-AS1" (0.002835)
60. feature "TPD52L1" (0.002831)
61. feature "TOP2A" (0.002822)
62. feature "TAF15" (0.002808)
63. feature "CEP63" (0.002793)
64. feature "DRAP1" (0.002748)
65. feature "S100A10" (0.002747)
66. feature "B4GALT1" (0.002716)
67. feature "MLLT3" (0.002694)
68. feature "MRFAP1" (0.002648)
69. feature "AK4" (0.002641)
70. feature "RPL41" (0.002634)
71. feature "ATN1" (0.002625)
72. feature "PIN4" (0.002613)
73. feature "SCP2" (0.002582)
74. feature "DNM2" (0.002567)
75. feature "NCL" (0.002551)
76. feature "ACSL4" (0.002541)
77. feature "RIPK1" (0.002532)
78. feature "APOOL" (0.002525)
79. feature "MT-ND6" (0.002510)
80. feature "CDT1" (0.002510)
81. feature "FEM1A" (0.002459)
82. feature "FAM114A1" (0.002455)
83. feature "LIMCH1" (0.002446)
84. feature "KIAA1522" (0.002432)
85. feature "HSP90AA1" (0.002413)
86. feature "CACNA1A" (0.002396)
87. feature "PTOV1" (0.002369)
88. feature "PRPF31" (0.002358)
89. feature "KLF6" (0.002334)
90. feature "TOP2B" (0.002313)
91. feature "SLC2A4RG" (0.002304)
92. feature "PPP4R2" (0.002303)
93. feature "ZBTB20" (0.002298)
94. feature "UPF3B" (0.002294)
95. feature "RPL39" (0.002280)
96. feature "ING2" (0.002280)
97. feature "ZNF324" (0.002267)
98. feature "PPP2R5D" (0.002259)
99. feature "SECISBP2" (0.002244)
100. feature "UCA1" (0.002216)
101. feature "MORN2" (0.002203)
102. feature "ZCRB1" (0.002201)
103. feature "DCAF7" (0.002190)
104. feature "UBE2E1" (0.002187)
105. feature "HMGA1" (0.002181)
106. feature "MT1X" (0.002171)
107. feature "PROSER1" (0.002169)
108. feature "CEP55" (0.002159)
109. feature "JUP" (0.002149)
110. feature "DDX23" (0.002148)
111. feature "BOD1L1" (0.002137)
112. feature "ARSA" (0.002136)
113. feature "COTL1" (0.002135)
114. feature "TLE3" (0.002134)
115. feature "MRPS6" (0.002134)
116. feature "MYO5C" (0.002133)
117. feature "PHF3" (0.002117)
118. feature "ZC3H15" (0.002114)
119. feature "RBM23" (0.002111)
120. feature "RPS3" (0.002111)
121. feature "PA2G4P6" (0.002110)
122. feature "NCLN" (0.002106)
123. feature "VEGFB" (0.002105)
124. feature "LINC01133" (0.002091)
125. feature "METTL5" (0.002090)
126. feature "CNNM2" (0.002088)
127. feature "SEM1" (0.002080)
128. feature "CUL4B" (0.002080)
129. feature "DDIT3" (0.002075)
130. feature "CHTOP" (0.002060)
131. feature "SMARCC1" (0.002058)
132. feature "TINAGL1" (0.002053)
133. feature "GNAS" (0.002044)
134. feature "CETN3" (0.002036)
135. feature "CAPN1" (0.002036)
136. feature "GDPGP1" (0.002016)
137. feature "SETD2" (0.002010)
138. feature "CLIP1" (0.002010)
139. feature "ARID5B" (0.001991)
140. feature "H2BC9" (0.001987)
141. feature "CTNND1" (0.001981)
142. feature "STT3A" (0.001975)
143. feature "PRRC2A" (0.001960)
144. feature "RPL7A" (0.001939)
145. feature "PDCD4" (0.001938)
146. feature "SAMD15" (0.001933)
147. feature "C12orf75" (0.001931)
148. feature "PLBD2" (0.001925)
149. feature "H2BC4" (0.001921)
150. feature "HEPACAM" (0.001915)
151. feature "ZNF302" (0.001913)
152. feature "PLEC" (0.001911)
153. feature "NSMF" (0.001903)
154. feature "H19" (0.001892)
155. feature "SYNJ2" (0.001878)
156. feature "RPS27" (0.001874)
157. feature "CLIC1" (0.001871)
158. feature "VMA21" (0.001865)
159. feature "SERINC2" (0.001861)
160. feature "DAAM1" (0.001857)
161. feature "CEP120" (0.001851)
162. feature "APPL1" (0.001849)
163. feature "MLXIP" (0.001848)
164. feature "H3C2" (0.001836)
165. feature "KMT5B" (0.001835)
166. feature "APLP2" (0.001829)
167. feature "RPS28" (0.001828)
168. feature "SHOX" (0.001825)
169. feature "AHNAK2" (0.001823)
170. feature "TEDC2-AS1" (0.001812)
171. feature "XRCC1" (0.001811)
172. feature "TAF13" (0.001804)
173. feature "RPLP0P2" (0.001792)
174. feature "PCIF1" (0.001791)
175. feature "CFAP251" (0.001783)
176. feature "EHBP1L1" (0.001778)
177. feature "AKT1S1" (0.001771)
178. feature "S100A11" (0.001764)
179. feature "RPL17" (0.001755)
180. feature "MDC1" (0.001752)
181. feature "TSR1" (0.001743)
182. feature "ERO1A" (0.001741)
183. feature "GPI" (0.001731)
184. feature "CCNI" (0.001709)
185. feature "MT-ND3" (0.001702)
186. feature "ANP32B" (0.001683)
187. feature "SOS1" (0.001655)
188. feature "PAQR7" (0.001647)
189. feature "NCALD" (0.001635)
190. feature "H4C5" (0.001625)
191. feature "RAB4A" (0.001606)
192. feature "H2AC11" (0.001595)
193. feature "MT-TQ" (0.001593)
194. feature "KRT19" (0.001591)
195. feature "NCOR2" (0.001588)
196. feature "MT-ND1" (0.001584)
197. feature "SLC2A1" (0.001580)
198. feature "NEDD1" (0.001578)
199. feature "RANGAP1" (0.001578)
200. feature "H2AC12" (0.001574)
201. feature "C4orf3" (0.001561)
202. feature "BICD1" (0.001559)
203. feature "CDK19" (0.001554)
204. feature "PABPC1" (0.001552)
205. feature "MLLT6" (0.001543)
206. feature "KCNJ3" (0.001542)
207. feature "KRT10" (0.001542)
208. feature "LMNB2" (0.001538)
209. feature "H1-2" (0.001537)
210. feature "NCOR1" (0.001525)
211. feature "ST14" (0.001525)
212. feature "KIF3B" (0.001513)
213. feature "BRMS1" (0.001511)
214. feature "SYT14" (0.001501)
215. feature "CTSZ" (0.001497)
216. feature "CALM2" (0.001493)
217. feature "SCPEP1" (0.001491)
218. feature "GNB1" (0.001487)
219. feature "WIPF2" (0.001486)
220. feature "RPS14" (0.001470)
221. feature "LINC02511" (0.001464)
222. feature "ENO1" (0.001463)
223. feature "GAPDH" (0.001455)
224. feature "RRAS" (0.001441)
225. feature "HRAS" (0.001438)
226. feature "CAMSAP2" (0.001436)
227. feature "MARCKS" (0.001430)
228. feature "TMA7" (0.001410)
229. feature "AK1" (0.001405)
230. feature "SIVA1" (0.001405)
231. feature "WBP4" (0.001400)
232. feature "PLOD2" (0.001399)
233. feature "FAM200B" (0.001399)
234. feature "MALAT1" (0.001397)
235. feature "RPLP1" (0.001393)
236. feature "CDC42BPA" (0.001390)
237. feature "NACA4P" (0.001388)
238. feature "ZNF318" (0.001386)
239. feature "SAMD4B" (0.001384)
240. feature "IGFL2-AS1" (0.001383)
241. feature "MT-ND5" (0.001382)
242. feature "HMGN3" (0.001382)
243. feature "PSMA7" (0.001380)
244. feature "EPPK1" (0.001379)
245. feature "LCA5" (0.001377)
246. feature "XPNPEP1" (0.001369)
247. feature "KRT13" (0.001358)
248. feature "GUK1" (0.001353)
249. feature "RPL18A" (0.001348)
250. feature "CMAS" (0.001340)
251. feature "AIP" (0.001313)
252. feature "TK1" (0.001311)
253. feature "FLNA" (0.001297)
254. feature "PGAM1" (0.001297)
255. feature "CAST" (0.001296)
256. feature "MT2A" (0.001292)
257. feature "THEM4" (0.001282)
258. feature "MLLT10" (0.001277)
259. feature "GRK2" (0.001272)
260. feature "HSBP1" (0.001256)
261. feature "ANAPC16" (0.001247)
262. feature "PPP1R21" (0.001246)
263. feature "RPL27A" (0.001243)
264. feature "SLC25A48" (0.001234)
265. feature "H2AX" (0.001229)
266. feature "FABP5" (0.001216)
267. feature "HSPBP1" (0.001208)
268. feature "FOXP1" (0.001206)
269. feature "AP1M1" (0.001198)
270. feature "METRN" (0.001196)
271. feature "SEPTIN9" (0.001194)
272. feature "UBC" (0.001185)
273. feature "GPM6B" (0.001184)
274. feature "LAMB3" (0.001178)
275. feature "CNOT6L" (0.001169)
276. feature "CSTA" (0.001168)
277. feature "GTF3A" (0.001168)
278. feature "CAVIN1" (0.001159)
279. feature "BAP1" (0.001158)
280. feature "RPL36" (0.001158)
281. feature "PPIG" (0.001151)
282. feature "IFI27L2" (0.001144)
283. feature "BTN3A2" (0.001143)
284. feature "DCTN1" (0.001142)
285. feature "SERF2" (0.001137)
286. feature "LARP7" (0.001135)
287. feature "FTL" (0.001122)
288. feature "LMF2" (0.001120)
289. feature "NUDT1" (0.001105)
290. feature "CAP1" (0.001103)
291. feature "SENP7" (0.001101)
292. feature "OTULINL" (0.001096)
293. feature "CAMK2N1" (0.001094)
294. feature "RPL10" (0.001093)
295. feature "S100A6" (0.001090)
296. feature "KIF5B" (0.001089)
297. feature "ATP5MG" (0.001082)
298. feature "TPI1" (0.001081)
299. feature "PDIA3" (0.001072)
300. feature "NDUFB10" (0.001067)
301. feature "EEF1A1" (0.001066)
302. feature "ENTR1" (0.001062)
303. feature "PDLIM1" (0.001061)
304. feature "MT-RNR1" (0.001060)
305. feature "YBX3" (0.001055)
306. feature "NIN" (0.001052)
307. feature "ELOB" (0.001037)
308. feature "HSP90AB1" (0.001030)
309. feature "CD47" (0.001028)
310. feature "H1-1" (0.001017)
311. feature "CANT1" (0.001015)
312. feature "PRR34-AS1" (0.001014)
313. feature "RPL35" (0.001007)
314. feature "SMARCB1" (0.001004)
315. feature "TCEAL4" (0.001001)
316. feature "EEF2" (0.001000)
317. feature "BRD4" (0.000996)
318. feature "ATP5MC2" (0.000991)
319. feature "PKP4" (0.000983)
320. feature "GIPC1" (0.000977)
321. feature "PRRG3" (0.000972)
322. feature "OPTN" (0.000969)
323. feature "MYH9" (0.000962)
324. feature "TOMM5" (0.000959)
325. feature "FAM162A" (0.000952)
326. feature "CREB3L2" (0.000951)
327. feature "ACTG1" (0.000938)
328. feature "CARMIL1" (0.000937)
329. feature "UBN2" (0.000937)
330. feature "MZT2B" (0.000935)
331. feature "TMSB4X" (0.000934)
332. feature "CAV1" (0.000933)
333. feature "RPL28" (0.000927)
334. feature "RPL37" (0.000925)
335. feature "KCNJ2" (0.000923)
336. feature "CMIP" (0.000919)
337. feature "TMEM238" (0.000918)
338. feature "ZNF688" (0.000917)
339. feature "GOLGA5" (0.000914)
340. feature "TPX2" (0.000913)
341. feature "RPL27" (0.000907)
342. feature "MKI67" (0.000904)
343. feature "UQCC2" (0.000903)
344. feature "SNHG6" (0.000903)
345. feature "POLR2H" (0.000902)
346. feature "METTL26" (0.000898)
347. feature "CFAP36" (0.000898)
348. feature "DNAAF5" (0.000898)
349. feature "UQCRQ" (0.000894)
350. feature "NEDD4L" (0.000894)
351. feature "FUBP3" (0.000893)
352. feature "HMGB2" (0.000891)
353. feature "NEFL" (0.000890)
354. feature "MT-CO2" (0.000887)
355. feature "HSPA5" (0.000886)
356. feature "ATP1A1" (0.000881)
357. feature "ANXA1" (0.000881)
358. feature "RPSA" (0.000876)
359. feature "H1-3" (0.000876)
360. feature "GPX4" (0.000874)
361. feature "PPDPF" (0.000874)
362. feature "YWHAZ" (0.000874)
363. feature "TPT1" (0.000873)
364. feature "BRWD3" (0.000871)
365. feature "DNMT1" (0.000865)
366. feature "HSP90B1" (0.000862)
367. feature "STRN4" (0.000859)
368. feature "SIGMAR1" (0.000857)
369. feature "PTGR1" (0.000857)
370. feature "PRDM2" (0.000851)
371. feature "H3-3A" (0.000851)
372. feature "RAB8B" (0.000850)
373. feature "MTA2" (0.000847)
374. feature "GBP1P1" (0.000846)
375. feature "MT-RNR2" (0.000843)
376. feature "LBH" (0.000839)
377. feature "FOSL2" (0.000833)
378. feature "MAFF" (0.000830)
379. feature "FCHO2" (0.000827)
380. feature "MSANTD3" (0.000826)
381. feature "UBE2I" (0.000825)
382. feature "CALR" (0.000824)
383. feature "PFN1" (0.000821)
384. feature "RTF1" (0.000820)
385. feature "C19orf53" (0.000819)
386. feature "KRT7" (0.000818)
387. feature "ATP5F1E" (0.000814)
388. feature "STMN1" (0.000808)
389. feature "CDK2AP1" (0.000807)
390. feature "CENPF" (0.000795)
391. feature "YTHDC1" (0.000793)
392. feature "MTND1P23" (0.000789)
393. feature "PAQR8" (0.000789)
394. feature "DTYMK" (0.000777)
395. feature "RNF10" (0.000777)
396. feature "TMEM160" (0.000777)
397. feature "RPL35A" (0.000776)
398. feature "MT-CO3" (0.000775)
399. feature "GNAI2" (0.000774)
400. feature "RPS6KA6" (0.000773)
401. feature "NMT2" (0.000772)
402. feature "SCNM1" (0.000772)
403. feature "RPS19" (0.000770)
404. feature "TAOK3" (0.000764)
405. feature "RPL12" (0.000763)
406. feature "LINC01304" (0.000756)
407. feature "PPP1R14B" (0.000754)
408. feature "PLEKHG3" (0.000750)
409. feature "OSBPL3" (0.000744)
410. feature "COPS9" (0.000743)
411. feature "RPS5" (0.000742)
412. feature "PKM" (0.000737)
413. feature "HCFC1R1" (0.000728)
414. feature "POLE4" (0.000727)
415. feature "ATAD2" (0.000719)
416. feature "ZHX1" (0.000719)
417. feature "RPS2" (0.000718)
418. feature "OAZ1" (0.000710)
419. feature "PTMS" (0.000699)
420. feature "S100A2" (0.000698)
421. feature "MAP7" (0.000698)
422. feature "PSMG1" (0.000696)
423. feature "DGKD" (0.000695)
424. feature "KDM1A" (0.000694)
425. feature "PDIA6" (0.000693)
426. feature "RASGEF1B" (0.000692)
427. feature "ADORA2B" (0.000692)
428. feature "ANP32A" (0.000686)
429. feature "DSP" (0.000682)
430. feature "NCAM1" (0.000682)
431. feature "TNS4" (0.000672)
432. feature "PHGDH" (0.000668)
433. feature "HDGF" (0.000667)
434. feature "CHCHD7" (0.000665)
435. feature "CBX3" (0.000664)
436. feature "KRT17" (0.000664)
437. feature "PRR11" (0.000657)
438. feature "NFE2L2" (0.000655)
439. feature "SPRYD4" (0.000654)
440. feature "TRIM44" (0.000650)
441. feature "UNK" (0.000649)
442. feature "RPS15" (0.000647)
443. feature "UBA3" (0.000646)
444. feature "POLR2L" (0.000646)
445. feature "SOX4" (0.000644)
446. feature "RAC1" (0.000642)
447. feature "SNRPA" (0.000639)
448. feature "RPL37A" (0.000636)
449. feature "DEK" (0.000634)
450. feature "RPL21" (0.000634)
451. feature "RPL18" (0.000631)
452. feature "LPAR4" (0.000631)
453. feature "RPL13" (0.000621)
454. feature "PFKP" (0.000620)
455. feature "EFNB2" (0.000619)
456. feature "HNRNPM" (0.000617)
457. feature "BIN1" (0.000614)
458. feature "H4C3" (0.000613)
459. feature "HSPB1" (0.000612)
460. feature "EZH1" (0.000610)
461. feature "RPS12" (0.000604)
462. feature "AHNAK" (0.000599)
463. feature "H2AZ2" (0.000598)
464. feature "MT-ND4L" (0.000596)
465. feature "MYL12B" (0.000592)
466. feature "YBX1" (0.000592)
467. feature "HNRNPA2B1" (0.000587)
468. feature "SRI" (0.000587)
469. feature "ARAP1" (0.000583)
470. feature "MCM4" (0.000582)
471. feature "NDUFB2" (0.000582)
472. feature "KRT14" (0.000582)
473. feature "RPLP2" (0.000578)
474. feature "GSTP1" (0.000576)
475. feature "RPS29" (0.000575)
476. feature "SGO2" (0.000574)
477. feature "CSTB" (0.000571)
478. feature "GOLGA3" (0.000570)
479. feature "CYB561D1" (0.000569)
480. feature "BACE2" (0.000568)
481. feature "EXOC7" (0.000565)
482. feature "NAB1" (0.000559)
483. feature "H1-4" (0.000559)
484. feature "PRMT2" (0.000555)
485. feature "MPHOSPH6" (0.000553)
486. feature "TANGO2" (0.000553)
487. feature "MT-ND4" (0.000551)
488. feature "USP53" (0.000548)
489. feature "LRRC59" (0.000547)
490. feature "CNTROB" (0.000545)
491. feature "GOLGA4" (0.000544)
492. feature "UQCR11" (0.000540)
493. feature "HNRNPU" (0.000530)
494. feature "EPB41L1" (0.000525)
495. feature "MINK1" (0.000521)
496. feature "PRR5L" (0.000519)
497. feature "RPS8" (0.000519)
498. feature "NMD3" (0.000512)
499. feature "CNR2" (0.000511)
500. feature "RAP2B" (0.000510)
501. feature "ZCCHC9" (0.000507)
502. feature "ODF2" (0.000504)
503. feature "CCDC34" (0.000504)
504. feature "FBXL19" (0.000504)
505. feature "HES1" (0.000499)
506. feature "HSPA4" (0.000495)
507. feature "NRBP1" (0.000493)
508. feature "TAGLN2" (0.000490)
509. feature "GNG5" (0.000488)
510. feature "SCRIB" (0.000485)
511. feature "TRPV4" (0.000481)
512. feature "FAM107B" (0.000478)
513. feature "SHISA5" (0.000478)
514. feature "DLG5" (0.000474)
515. feature "JUN" (0.000473)
516. feature "MZT2A" (0.000473)
517. feature "DPCD" (0.000471)
518. feature "SFN" (0.000468)
519. feature "EZR" (0.000466)
520. feature "CIRBP" (0.000465)
521. feature "PUS7" (0.000464)
522. feature "HCFC1" (0.000460)
523. feature "PAIP2" (0.000445)
524. feature "CDK14" (0.000431)
525. feature "MAPK12" (0.000424)
526. feature "KRT5" (0.000420)
527. feature "UNC45A" (0.000418)
528. feature "RPL22L1" (0.000418)
529. feature "KAT7" (0.000413)
530. feature "RPL8" (0.000412)
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532. feature "MRPS33" (0.000410)
533. feature "SNRNP70" (0.000409)
534. feature "NARF" (0.000408)
535. feature "ATP2A2" (0.000405)
536. feature "SRC" (0.000402)
537. feature "UBE2C" (0.000401)
538. feature "TTC19" (0.000400)
539. feature "GSK3A" (0.000397)
540. feature "LRRC20" (0.000392)
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542. feature "H2AW" (0.000388)
543. feature "NDUFC1" (0.000387)
544. feature "MT-ND2" (0.000386)
545. feature "CRPPA" (0.000385)
546. feature "LRRFIP2" (0.000385)
547. feature "TMEM70" (0.000383)
548. feature "DMD" (0.000378)
549. feature "ZRANB1" (0.000376)
550. feature "RPS21" (0.000373)
551. feature "VPS45" (0.000372)
552. feature "CDKN3" (0.000370)
553. feature "MT-ATP6" (0.000370)
554. feature "LRRIQ1" (0.000369)
555. feature "DEPDC1B" (0.000365)
556. feature "ZFHX3" (0.000360)
557. feature "SH3RF1" (0.000358)
558. feature "PSIP1" (0.000354)
559. feature "PPP2R1A" (0.000353)
560. feature "DDX54" (0.000352)
561. feature "ZMYND8" (0.000352)
562. feature "INHBA" (0.000350)
563. feature "MRPL33" (0.000350)
564. feature "TOLLIP" (0.000342)
565. feature "UACA" (0.000337)
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567. feature "ATOX1" (0.000320)
568. feature "SLC25A37" (0.000320)
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570. feature "BAHD1" (0.000308)
571. feature "TUBB6" (0.000303)
572. feature "EGR1" (0.000300)
573. feature "CDK2AP2" (0.000300)
574. feature "IPO9" (0.000296)
575. feature "JUND" (0.000275)
576. feature "EHD2" (0.000270)
577. feature "MRPL55" (0.000257)
578. feature "ATP5ME" (0.000257)
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580. feature "RAD54L2" (0.000246)
581. feature "TFE3" (0.000243)
582. feature "RHOA" (0.000243)
583. feature "RBAK" (0.000241)
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585. feature "COMMD6" (0.000237)
586. feature "MDFI" (0.000235)
587. feature "KIF9" (0.000231)
588. feature "LEO1" (0.000216)
589. feature "HOXB7" (0.000216)
590. feature "ZBED4" (0.000205)
591. feature "TWNK" (0.000199)
592. feature "NDUFB1" (0.000176)
593. feature "CAPZB" (0.000167)
594. feature "NR2F6" (0.000159)
595. feature "POLR2A" (0.000159)
596. feature "SAMD4A" (0.000145)
597. feature "IMP3" (0.000118)
598. feature "FAM83A" (0.000106)
599. feature "UIMC1" (0.000104)
600. feature "LAGE3" (0.000103)
601. feature "TCEAL9" (0.000102)
602. feature "DOHH" (0.000099)
603. feature "BRCC3" (0.000099)
604. feature "KHNYN" (0.000094)
605. feature "PPP1R13L" (0.000079)
606. feature "BMPR1B" (0.000076)
607. feature "VAT1" (0.000062)
608. feature "PHLDB2" (0.000052)
609. feature "IRAK1" (0.000049)
610. feature "TF" (0.000025)
611. feature "MAP3K4" (0.000025)
612. feature "NIBAN2" (0.000010)
613. feature "TMEM141" (0.000003)
614. feature "FGFR1OP2" (0.000001)
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632. feature "IQANK1" (0.000000)
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638. feature "SLC40A1" (0.000000)
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646. feature "BLCAP" (0.000000)
647. feature "TLE2" (0.000000)
648. feature "RAB42" (0.000000)
649. feature "TNFRSF12A" (0.000000)
650. feature "ZNF132" (0.000000)
651. feature "PPP2R3B" (0.000000)
652. feature "WAC-AS1" (0.000000)
653. feature "C16orf95-DT" (0.000000)
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655. feature "CACNB3" (0.000000)
656. feature "ABT1" (0.000000)
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2876. feature "BAMBI" (0.000000)
2877. feature "NUDT8" (0.000000)
2878. feature "APLF" (0.000000)
2879. feature "TCP11L1" (0.000000)
2880. feature "CBX6" (0.000000)
2881. feature "SLC8A1" (0.000000)
2882. feature "ITGA3" (0.000000)
2883. feature "TCF15" (0.000000)
2884. feature "LINC02057" (0.000000)
2885. feature "ATF3" (0.000000)
2886. feature "MMP2" (0.000000)
2887. feature "ITSN1" (0.000000)
2888. feature "FLAD1" (0.000000)
2889. feature "P2RY2" (0.000000)
2890. feature "ALDOC" (0.000000)
2891. feature "NLRX1" (0.000000)
2892. feature "ZNF446" (0.000000)
2893. feature "KIF16B" (0.000000)
2894. feature "DOC2A" (0.000000)
2895. feature "CENPBD1" (0.000000)
2896. feature "CDK9" (0.000000)
2897. feature "RAVER1" (0.000000)
2898. feature "MSRB3" (0.000000)
2899. feature "PLD5" (0.000000)
2900. feature "RNF19B" (0.000000)
2901. feature "CBWD3" (0.000000)
2902. feature "NEBL" (0.000000)
2903. feature "TBC1D22A" (0.000000)
2904. feature "GNG4" (0.000000)
2905. feature "IL12B" (0.000000)
2906. feature "B4GALT2" (0.000000)
2907. feature "MT-TH" (0.000000)
2908. feature "ZNF469" (0.000000)
2909. feature "ITPKC" (0.000000)
2910. feature "MYH14" (0.000000)
2911. feature "DOCK9" (0.000000)
2912. feature "CDH24" (0.000000)
2913. feature "DCAF10" (0.000000)
2914. feature "CCDC168" (0.000000)
2915. feature "LINC02672" (0.000000)
2916. feature "C19orf81" (0.000000)
2917. feature "MBD1" (0.000000)
2918. feature "CDPF1" (0.000000)
2919. feature "KHK" (0.000000)
2920. feature "TCF3" (0.000000)
2921. feature "GRIN2D" (0.000000)
2922. feature "IFNGR2" (0.000000)
2923. feature "ATP6V1B2" (0.000000)
2924. feature "SPINK5" (0.000000)
2925. feature "FAM13B" (0.000000)
2926. feature "CAPNS1" (0.000000)
2927. feature "TAF10" (0.000000)
2928. feature "S1PR3" (0.000000)
2929. feature "HOXA11-AS" (0.000000)
2930. feature "MIR663B" (0.000000)
2931. feature "LINC00911" (0.000000)
2932. feature "YBEY" (0.000000)
2933. feature "ZNF268" (0.000000)
2934. feature "MORC3" (0.000000)
2935. feature "RNASEH1-AS1" (0.000000)
2936. feature "SLC35B3" (0.000000)
2937. feature "PFKFB3" (0.000000)
2938. feature "MXRA5" (0.000000)
2939. feature "ETAA1" (0.000000)
2940. feature "ZYG11A" (0.000000)
2941. feature "PPTC7" (0.000000)
2942. feature "FRAT2" (0.000000)
2943. feature "SDSL" (0.000000)
2944. feature "NUMA1" (0.000000)
2945. feature "C19orf54" (0.000000)
2946. feature "KDM3A" (0.000000)
2947. feature "KIRREL1" (0.000000)
2948. feature "SLC9A3" (0.000000)
2949. feature "ZNF33B" (0.000000)
2950. feature "RN7SL471P" (0.000000)
2951. feature "DPRX" (0.000000)
2952. feature "C17orf99" (0.000000)
2953. feature "PIK3R2" (0.000000)
2954. feature "ARHGEF28" (0.000000)
2955. feature "PEX11A" (0.000000)
2956. feature "OXNAD1" (0.000000)
2957. feature "TONSL" (0.000000)
2958. feature "HERC1" (0.000000)
2959. feature "ACVR2B" (0.000000)
2960. feature "FHDC1" (0.000000)
2961. feature "ZBTB47" (0.000000)
2962. feature "MAML1" (0.000000)
2963. feature "PCDHGA10" (0.000000)
2964. feature "IL18" (0.000000)
2965. feature "RGS10" (0.000000)
2966. feature "S100A4" (0.000000)
2967. feature "DOP1B" (0.000000)
2968. feature "TRMT5" (0.000000)
2969. feature "PATZ1" (0.000000)
2970. feature "MON1B" (0.000000)
2971. feature "LTBP4" (0.000000)
2972. feature "TNFAIP8L1" (0.000000)
2973. feature "ZBTB38" (0.000000)
2974. feature "OVCH1-AS1" (0.000000)
2975. feature "IL6R" (0.000000)
2976. feature "PXMP4" (0.000000)
2977. feature "KCNMB4" (0.000000)
2978. feature "DHX35-DT" (0.000000)
2979. feature "MAGEA4" (0.000000)
2980. feature "PTCH1" (0.000000)
2981. feature "TGDS" (0.000000)
2982. feature "MAGI2-AS3" (0.000000)
2983. feature "PPIH" (0.000000)
2984. feature "VPS26B" (0.000000)
2985. feature "FKBP9P1" (0.000000)
2986. feature "WDR18" (0.000000)
2987. feature "CAVIN2" (0.000000)
2988. feature "IDH3G" (0.000000)
2989. feature "NEUROD2" (0.000000)
2990. feature "PKN1" (0.000000)
2991. feature "RHBDL2" (0.000000)
2992. feature "C16orf74" (0.000000)
2993. feature "MCRIP1" (0.000000)
2994. feature "GJD2" (0.000000)
2995. feature "HMBOX1" (0.000000)
2996. feature "CTU2" (0.000000)
2997. feature "CCDC191" (0.000000)
2998. feature "EPHX1" (0.000000)
2999. feature "GREP1" (0.000000)
3000. feature "TROAP" (0.000000)
¶
4.4.4) Neural Network
We now define the neural network model to be used with the dataset dropseq HCC1806
# This is the model (neural network) applied to the dataset dropseq HCC1806
model_DS_H = NeuralNetworkDropSeq()
model_DS_H
NeuralNetworkDropSeq(
(model): Sequential(
(0): Flatten(start_dim=1, end_dim=-1)
(1): Linear(in_features=3000, out_features=512, bias=True)
(2): LeakyReLU(negative_slope=0.01)
(3): Dropout(p=0.4, inplace=False)
(4): Linear(in_features=512, out_features=256, bias=True)
(5): LeakyReLU(negative_slope=0.01)
(6): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): Dropout(p=0.4, inplace=False)
(8): Linear(in_features=256, out_features=128, bias=True)
(9): LeakyReLU(negative_slope=0.01)
(10): Dropout(p=0.3, inplace=False)
(11): Linear(in_features=128, out_features=64, bias=True)
(12): LeakyReLU(negative_slope=0.01)
(13): Linear(in_features=64, out_features=1, bias=True)
)
)
As we did for the MCF7 dataset, also here we split the train dataset and the labels into train dataset and validation dataset (this second one used to validate and see the accuracy during the training phase). Then we convert these variables into tensors in order to be able to use them with pytorch.
# Convert the split datasets into tensors
X_train_DS_HCC1806_nn, X_val_DS_HCC1806_nn, y_train_DS_HCC1806_nn, y_val_DS_HCC1806_nn = train_test_split(X_train_df_DS_HCC1806.values, y_train_df_DS_HCC1806.values, test_size=0.2, random_state=42)
X_train_tensor_H = torch.tensor(X_train_DS_HCC1806_nn, dtype=torch.float32)
y_train_tensor_H = torch.tensor(y_train_DS_HCC1806_nn, dtype=torch.float32).unsqueeze(1)
X_val_tensor_H = torch.tensor(X_val_DS_HCC1806_nn, dtype=torch.float32)
y_val_tensor_H = torch.tensor(y_val_DS_HCC1806_nn, dtype=torch.float32).unsqueeze(1) # For binary classification
¶
Training for the model drop-seq HCC1806
Here below there is the training process of the neural network using the dataset HCC1806 dropseq.
It follows the same structure of the training process used with the MCF7 dataset; therefore we use Stochastic Gradient Descent (SGD) as the optimizer with a learning rate of 0.01, and as a loss function the binary cross-entropy with logits ('BCEWithLogitsLoss'). Also here, the training runs for 1000 epochs.
The training loop starts by setting the model to training mode, resetting the gradients, performing a forward pass on the training data, calculating the training loss, and then using backpropagation to compute the gradients. The optimizer updates the model parameters based on these gradients. Finally, training accuracy is calculated by rounding the sigmoid-activated predictions and comparing them to the true labels.
For validation, the model is set to evaluation mode, and a forward pass is performed on the validation data. The validation loss and accuracy are calculated similarly to the training process but without computing gradients.
optimizer = torch.optim.SGD(model_DS_H.parameters(), lr=0.01)
criterion = nn.BCEWithLogitsLoss() # applies a sigmoid activation to the model's raw logits and calculates the binary cross-entropy loss
epochs = 1000
best_val_acc = 0
patience = 3
no_improve_epochs = 0
best_model_path_H = 'best_model_final_H.pth'
for epoch in range(epochs):
model_DS_H.train()
optimizer.zero_grad() # resets the gradients of all model parameters to zero.
train_output = model_DS_H(X_train_tensor_H) # Forward pass with all training data, computes the model's predictions (output) for the entire training dataset
train_loss = criterion(train_output, y_train_tensor_H) # calculates the difference between predicted outputs and actual labels
train_loss.backward() #computes the gradients of the loss with respect to the model's parameters
# This uses backpropagation to distribute the error back through the network
optimizer.step() #The optimizer applies the selected algorithm (like SGD) to update the weights
# Calculate training accuracy
with torch.no_grad():
train_predictions = torch.round(torch.sigmoid(train_output))
train_correct = (train_predictions == y_train_tensor_H).sum().item()
train_total = y_train_tensor_H.size(0)
train_acc = train_correct / train_total
# Validation
model_DS_H.eval() # sets the model to evaluation mode, disabling features like dropout and batch normalization to ensure consistency
with torch.no_grad(): #prevents PyTorch from computing gradients during evaluation.
val_output = model_DS_H(X_val_tensor_H) #computes predictions on the validation dataset
val_loss = criterion(val_output, y_val_tensor_H)
val_predictions = torch.round(torch.sigmoid(val_output)) # converts the logits/predictions into binary predictions (0 or 1)
val_correct = (val_predictions == y_val_tensor_H).sum().item()
val_total = y_val_tensor_H.size(0)
val_acc = val_correct / val_total
print(f'Epoch {epoch+1}/{epochs}, '
f'Train Loss: {train_loss.item():.4f}, '
f'Train Accuracy: {train_acc:.4f}, '
f'Val Loss: {val_loss.item():.4f}, '
f'Val Accuracy: {val_acc:.4f}')
# Save model after all epochs are completed
#torch.save(model.state_dict(), best_model_path)
Epoch 1/1000, Train Loss: 0.7117, Train Accuracy: 0.4291, Val Loss: 0.7004, Val Accuracy: 0.4110 Epoch 2/1000, Train Loss: 0.7104, Train Accuracy: 0.4312, Val Loss: 0.7002, Val Accuracy: 0.4110 Epoch 3/1000, Train Loss: 0.7091, Train Accuracy: 0.4357, Val Loss: 0.6999, Val Accuracy: 0.4110 Epoch 4/1000, Train Loss: 0.7065, Train Accuracy: 0.4475, Val Loss: 0.6997, Val Accuracy: 0.4110 Epoch 5/1000, Train Loss: 0.7058, Train Accuracy: 0.4430, Val Loss: 0.6995, Val Accuracy: 0.4110 Epoch 6/1000, Train Loss: 0.7060, Train Accuracy: 0.4469, Val Loss: 0.6992, Val Accuracy: 0.4110 Epoch 7/1000, Train Loss: 0.7020, Train Accuracy: 0.4620, Val Loss: 0.6990, Val Accuracy: 0.4110 Epoch 8/1000, Train Loss: 0.7011, Train Accuracy: 0.4722, Val Loss: 0.6988, Val Accuracy: 0.4110 Epoch 9/1000, Train Loss: 0.6997, Train Accuracy: 0.4728, Val Loss: 0.6985, Val Accuracy: 0.4110 Epoch 10/1000, Train Loss: 0.6980, Train Accuracy: 0.4814, Val Loss: 0.6983, Val Accuracy: 0.4110 Epoch 11/1000, Train Loss: 0.6983, Train Accuracy: 0.4841, Val Loss: 0.6980, Val Accuracy: 0.4110 Epoch 12/1000, Train Loss: 0.6972, Train Accuracy: 0.4852, Val Loss: 0.6978, Val Accuracy: 0.4110 Epoch 13/1000, Train Loss: 0.6961, Train Accuracy: 0.4922, Val Loss: 0.6975, Val Accuracy: 0.4110 Epoch 14/1000, Train Loss: 0.6945, Train Accuracy: 0.5033, Val Loss: 0.6973, Val Accuracy: 0.4110 Epoch 15/1000, Train Loss: 0.6932, Train Accuracy: 0.5144, Val Loss: 0.6970, Val Accuracy: 0.4110 Epoch 16/1000, Train Loss: 0.6924, Train Accuracy: 0.5102, Val Loss: 0.6967, Val Accuracy: 0.4110 Epoch 17/1000, Train Loss: 0.6899, Train Accuracy: 0.5273, Val Loss: 0.6965, Val Accuracy: 0.4110 Epoch 18/1000, Train Loss: 0.6903, Train Accuracy: 0.5323, Val Loss: 0.6962, Val Accuracy: 0.4110 Epoch 19/1000, Train Loss: 0.6890, Train Accuracy: 0.5434, Val Loss: 0.6959, Val Accuracy: 0.4110 Epoch 20/1000, Train Loss: 0.6891, Train Accuracy: 0.5379, Val Loss: 0.6956, Val Accuracy: 0.4110 Epoch 21/1000, Train Loss: 0.6874, Train Accuracy: 0.5403, Val Loss: 0.6953, Val Accuracy: 0.4110 Epoch 22/1000, Train Loss: 0.6862, Train Accuracy: 0.5580, Val Loss: 0.6949, Val Accuracy: 0.4110 Epoch 23/1000, Train Loss: 0.6841, Train Accuracy: 0.5734, Val Loss: 0.6946, Val Accuracy: 0.4110 Epoch 24/1000, Train Loss: 0.6834, Train Accuracy: 0.5746, Val Loss: 0.6942, Val Accuracy: 0.4115 Epoch 25/1000, Train Loss: 0.6828, Train Accuracy: 0.5738, Val Loss: 0.6939, Val Accuracy: 0.4115 Epoch 26/1000, Train Loss: 0.6809, Train Accuracy: 0.5900, Val Loss: 0.6935, Val Accuracy: 0.4163 Epoch 27/1000, Train Loss: 0.6806, Train Accuracy: 0.5913, Val Loss: 0.6931, Val Accuracy: 0.4246 Epoch 28/1000, Train Loss: 0.6781, Train Accuracy: 0.6013, Val Loss: 0.6927, Val Accuracy: 0.4334 Epoch 29/1000, Train Loss: 0.6782, Train Accuracy: 0.6039, Val Loss: 0.6923, Val Accuracy: 0.4548 Epoch 30/1000, Train Loss: 0.6777, Train Accuracy: 0.6061, Val Loss: 0.6918, Val Accuracy: 0.4669 Epoch 31/1000, Train Loss: 0.6759, Train Accuracy: 0.6176, Val Loss: 0.6913, Val Accuracy: 0.4864 Epoch 32/1000, Train Loss: 0.6749, Train Accuracy: 0.6196, Val Loss: 0.6908, Val Accuracy: 0.5088 Epoch 33/1000, Train Loss: 0.6746, Train Accuracy: 0.6232, Val Loss: 0.6903, Val Accuracy: 0.5370 Epoch 34/1000, Train Loss: 0.6732, Train Accuracy: 0.6285, Val Loss: 0.6898, Val Accuracy: 0.5705 Epoch 35/1000, Train Loss: 0.6722, Train Accuracy: 0.6305, Val Loss: 0.6892, Val Accuracy: 0.5948 Epoch 36/1000, Train Loss: 0.6704, Train Accuracy: 0.6448, Val Loss: 0.6886, Val Accuracy: 0.6289 Epoch 37/1000, Train Loss: 0.6700, Train Accuracy: 0.6492, Val Loss: 0.6880, Val Accuracy: 0.6508 Epoch 38/1000, Train Loss: 0.6695, Train Accuracy: 0.6483, Val Loss: 0.6873, Val Accuracy: 0.6761 Epoch 39/1000, Train Loss: 0.6675, Train Accuracy: 0.6528, Val Loss: 0.6866, Val Accuracy: 0.6960 Epoch 40/1000, Train Loss: 0.6677, Train Accuracy: 0.6483, Val Loss: 0.6859, Val Accuracy: 0.7087 Epoch 41/1000, Train Loss: 0.6656, Train Accuracy: 0.6626, Val Loss: 0.6851, Val Accuracy: 0.7247 Epoch 42/1000, Train Loss: 0.6647, Train Accuracy: 0.6652, Val Loss: 0.6844, Val Accuracy: 0.7339 Epoch 43/1000, Train Loss: 0.6654, Train Accuracy: 0.6575, Val Loss: 0.6836, Val Accuracy: 0.7466 Epoch 44/1000, Train Loss: 0.6637, Train Accuracy: 0.6678, Val Loss: 0.6827, Val Accuracy: 0.7617 Epoch 45/1000, Train Loss: 0.6638, Train Accuracy: 0.6584, Val Loss: 0.6819, Val Accuracy: 0.7680 Epoch 46/1000, Train Loss: 0.6610, Train Accuracy: 0.6752, Val Loss: 0.6810, Val Accuracy: 0.7753 Epoch 47/1000, Train Loss: 0.6602, Train Accuracy: 0.6730, Val Loss: 0.6800, Val Accuracy: 0.7826 Epoch 48/1000, Train Loss: 0.6604, Train Accuracy: 0.6718, Val Loss: 0.6791, Val Accuracy: 0.7875 Epoch 49/1000, Train Loss: 0.6577, Train Accuracy: 0.6819, Val Loss: 0.6780, Val Accuracy: 0.7928 Epoch 50/1000, Train Loss: 0.6577, Train Accuracy: 0.6763, Val Loss: 0.6770, Val Accuracy: 0.7986 Epoch 51/1000, Train Loss: 0.6558, Train Accuracy: 0.6926, Val Loss: 0.6759, Val Accuracy: 0.7972 Epoch 52/1000, Train Loss: 0.6548, Train Accuracy: 0.6847, Val Loss: 0.6749, Val Accuracy: 0.8030 Epoch 53/1000, Train Loss: 0.6552, Train Accuracy: 0.6834, Val Loss: 0.6737, Val Accuracy: 0.8054 Epoch 54/1000, Train Loss: 0.6535, Train Accuracy: 0.6901, Val Loss: 0.6726, Val Accuracy: 0.8074 Epoch 55/1000, Train Loss: 0.6518, Train Accuracy: 0.6921, Val Loss: 0.6715, Val Accuracy: 0.8054 Epoch 56/1000, Train Loss: 0.6523, Train Accuracy: 0.6886, Val Loss: 0.6703, Val Accuracy: 0.8069 Epoch 57/1000, Train Loss: 0.6500, Train Accuracy: 0.6904, Val Loss: 0.6691, Val Accuracy: 0.8089 Epoch 58/1000, Train Loss: 0.6491, Train Accuracy: 0.6870, Val Loss: 0.6679, Val Accuracy: 0.8108 Epoch 59/1000, Train Loss: 0.6483, Train Accuracy: 0.6933, Val Loss: 0.6666, Val Accuracy: 0.8113 Epoch 60/1000, Train Loss: 0.6478, Train Accuracy: 0.6887, Val Loss: 0.6654, Val Accuracy: 0.8142 Epoch 61/1000, Train Loss: 0.6456, Train Accuracy: 0.6983, Val Loss: 0.6642, Val Accuracy: 0.8142 Epoch 62/1000, Train Loss: 0.6443, Train Accuracy: 0.6988, Val Loss: 0.6629, Val Accuracy: 0.8181 Epoch 63/1000, Train Loss: 0.6444, Train Accuracy: 0.6988, Val Loss: 0.6617, Val Accuracy: 0.8196 Epoch 64/1000, Train Loss: 0.6428, Train Accuracy: 0.7044, Val Loss: 0.6604, Val Accuracy: 0.8220 Epoch 65/1000, Train Loss: 0.6417, Train Accuracy: 0.6987, Val Loss: 0.6591, Val Accuracy: 0.8234 Epoch 66/1000, Train Loss: 0.6420, Train Accuracy: 0.6981, Val Loss: 0.6579, Val Accuracy: 0.8225 Epoch 67/1000, Train Loss: 0.6396, Train Accuracy: 0.7059, Val Loss: 0.6567, Val Accuracy: 0.8225 Epoch 68/1000, Train Loss: 0.6398, Train Accuracy: 0.7009, Val Loss: 0.6554, Val Accuracy: 0.8230 Epoch 69/1000, Train Loss: 0.6382, Train Accuracy: 0.6983, Val Loss: 0.6541, Val Accuracy: 0.8239 Epoch 70/1000, Train Loss: 0.6374, Train Accuracy: 0.7004, Val Loss: 0.6529, Val Accuracy: 0.8254 Epoch 71/1000, Train Loss: 0.6358, Train Accuracy: 0.7026, Val Loss: 0.6516, Val Accuracy: 0.8259 Epoch 72/1000, Train Loss: 0.6329, Train Accuracy: 0.7023, Val Loss: 0.6504, Val Accuracy: 0.8273 Epoch 73/1000, Train Loss: 0.6341, Train Accuracy: 0.7077, Val Loss: 0.6492, Val Accuracy: 0.8278 Epoch 74/1000, Train Loss: 0.6336, Train Accuracy: 0.6975, Val Loss: 0.6479, Val Accuracy: 0.8278 Epoch 75/1000, Train Loss: 0.6309, Train Accuracy: 0.7043, Val Loss: 0.6467, Val Accuracy: 0.8288 Epoch 76/1000, Train Loss: 0.6302, Train Accuracy: 0.6989, Val Loss: 0.6454, Val Accuracy: 0.8293 Epoch 77/1000, Train Loss: 0.6302, Train Accuracy: 0.6999, Val Loss: 0.6442, Val Accuracy: 0.8303 Epoch 78/1000, Train Loss: 0.6276, Train Accuracy: 0.7071, Val Loss: 0.6430, Val Accuracy: 0.8312 Epoch 79/1000, Train Loss: 0.6283, Train Accuracy: 0.7033, Val Loss: 0.6417, Val Accuracy: 0.8317 Epoch 80/1000, Train Loss: 0.6266, Train Accuracy: 0.7049, Val Loss: 0.6405, Val Accuracy: 0.8317 Epoch 81/1000, Train Loss: 0.6238, Train Accuracy: 0.7096, Val Loss: 0.6392, Val Accuracy: 0.8307 Epoch 82/1000, Train Loss: 0.6238, Train Accuracy: 0.7025, Val Loss: 0.6380, Val Accuracy: 0.8317 Epoch 83/1000, Train Loss: 0.6232, Train Accuracy: 0.7045, Val Loss: 0.6367, Val Accuracy: 0.8317 Epoch 84/1000, Train Loss: 0.6209, Train Accuracy: 0.7093, Val Loss: 0.6355, Val Accuracy: 0.8317 Epoch 85/1000, Train Loss: 0.6203, Train Accuracy: 0.7049, Val Loss: 0.6343, Val Accuracy: 0.8317 Epoch 86/1000, Train Loss: 0.6190, Train Accuracy: 0.7103, Val Loss: 0.6330, Val Accuracy: 0.8341 Epoch 87/1000, Train Loss: 0.6178, Train Accuracy: 0.7087, Val Loss: 0.6318, Val Accuracy: 0.8341 Epoch 88/1000, Train Loss: 0.6171, Train Accuracy: 0.7066, Val Loss: 0.6305, Val Accuracy: 0.8356 Epoch 89/1000, Train Loss: 0.6154, Train Accuracy: 0.7090, Val Loss: 0.6292, Val Accuracy: 0.8346 Epoch 90/1000, Train Loss: 0.6152, Train Accuracy: 0.7106, Val Loss: 0.6279, Val Accuracy: 0.8366 Epoch 91/1000, Train Loss: 0.6124, Train Accuracy: 0.7090, Val Loss: 0.6267, Val Accuracy: 0.8385 Epoch 92/1000, Train Loss: 0.6123, Train Accuracy: 0.7133, Val Loss: 0.6253, Val Accuracy: 0.8380 Epoch 93/1000, Train Loss: 0.6097, Train Accuracy: 0.7150, Val Loss: 0.6241, Val Accuracy: 0.8380 Epoch 94/1000, Train Loss: 0.6091, Train Accuracy: 0.7126, Val Loss: 0.6227, Val Accuracy: 0.8385 Epoch 95/1000, Train Loss: 0.6078, Train Accuracy: 0.7099, Val Loss: 0.6214, Val Accuracy: 0.8400 Epoch 96/1000, Train Loss: 0.6064, Train Accuracy: 0.7143, Val Loss: 0.6200, Val Accuracy: 0.8405 Epoch 97/1000, Train Loss: 0.6048, Train Accuracy: 0.7179, Val Loss: 0.6187, Val Accuracy: 0.8405 Epoch 98/1000, Train Loss: 0.6034, Train Accuracy: 0.7180, Val Loss: 0.6174, Val Accuracy: 0.8400 Epoch 99/1000, Train Loss: 0.6037, Train Accuracy: 0.7120, Val Loss: 0.6160, Val Accuracy: 0.8405 Epoch 100/1000, Train Loss: 0.6018, Train Accuracy: 0.7176, Val Loss: 0.6146, Val Accuracy: 0.8410 Epoch 101/1000, Train Loss: 0.6002, Train Accuracy: 0.7184, Val Loss: 0.6132, Val Accuracy: 0.8410 Epoch 102/1000, Train Loss: 0.5979, Train Accuracy: 0.7162, Val Loss: 0.6118, Val Accuracy: 0.8405 Epoch 103/1000, Train Loss: 0.5976, Train Accuracy: 0.7184, Val Loss: 0.6104, Val Accuracy: 0.8410 Epoch 104/1000, Train Loss: 0.5958, Train Accuracy: 0.7177, Val Loss: 0.6090, Val Accuracy: 0.8410 Epoch 105/1000, Train Loss: 0.5944, Train Accuracy: 0.7202, Val Loss: 0.6075, Val Accuracy: 0.8434 Epoch 106/1000, Train Loss: 0.5943, Train Accuracy: 0.7249, Val Loss: 0.6061, Val Accuracy: 0.8439 Epoch 107/1000, Train Loss: 0.5918, Train Accuracy: 0.7241, Val Loss: 0.6046, Val Accuracy: 0.8439 Epoch 108/1000, Train Loss: 0.5909, Train Accuracy: 0.7241, Val Loss: 0.6031, Val Accuracy: 0.8458 Epoch 109/1000, Train Loss: 0.5899, Train Accuracy: 0.7257, Val Loss: 0.6017, Val Accuracy: 0.8463 Epoch 110/1000, Train Loss: 0.5875, Train Accuracy: 0.7270, Val Loss: 0.6001, Val Accuracy: 0.8468 Epoch 111/1000, Train Loss: 0.5855, Train Accuracy: 0.7335, Val Loss: 0.5987, Val Accuracy: 0.8478 Epoch 112/1000, Train Loss: 0.5860, Train Accuracy: 0.7231, Val Loss: 0.5972, Val Accuracy: 0.8482 Epoch 113/1000, Train Loss: 0.5815, Train Accuracy: 0.7321, Val Loss: 0.5957, Val Accuracy: 0.8492 Epoch 114/1000, Train Loss: 0.5804, Train Accuracy: 0.7337, Val Loss: 0.5940, Val Accuracy: 0.8502 Epoch 115/1000, Train Loss: 0.5796, Train Accuracy: 0.7394, Val Loss: 0.5922, Val Accuracy: 0.8497 Epoch 116/1000, Train Loss: 0.5795, Train Accuracy: 0.7340, Val Loss: 0.5906, Val Accuracy: 0.8502 Epoch 117/1000, Train Loss: 0.5756, Train Accuracy: 0.7394, Val Loss: 0.5890, Val Accuracy: 0.8507 Epoch 118/1000, Train Loss: 0.5762, Train Accuracy: 0.7397, Val Loss: 0.5873, Val Accuracy: 0.8517 Epoch 119/1000, Train Loss: 0.5737, Train Accuracy: 0.7401, Val Loss: 0.5857, Val Accuracy: 0.8512 Epoch 120/1000, Train Loss: 0.5719, Train Accuracy: 0.7418, Val Loss: 0.5839, Val Accuracy: 0.8521 Epoch 121/1000, Train Loss: 0.5709, Train Accuracy: 0.7454, Val Loss: 0.5822, Val Accuracy: 0.8517 Epoch 122/1000, Train Loss: 0.5682, Train Accuracy: 0.7435, Val Loss: 0.5806, Val Accuracy: 0.8517 Epoch 123/1000, Train Loss: 0.5674, Train Accuracy: 0.7452, Val Loss: 0.5791, Val Accuracy: 0.8512 Epoch 124/1000, Train Loss: 0.5649, Train Accuracy: 0.7495, Val Loss: 0.5774, Val Accuracy: 0.8521 Epoch 125/1000, Train Loss: 0.5640, Train Accuracy: 0.7474, Val Loss: 0.5757, Val Accuracy: 0.8546 Epoch 126/1000, Train Loss: 0.5617, Train Accuracy: 0.7502, Val Loss: 0.5740, Val Accuracy: 0.8551 Epoch 127/1000, Train Loss: 0.5600, Train Accuracy: 0.7547, Val Loss: 0.5722, Val Accuracy: 0.8560 Epoch 128/1000, Train Loss: 0.5596, Train Accuracy: 0.7525, Val Loss: 0.5703, Val Accuracy: 0.8565 Epoch 129/1000, Train Loss: 0.5565, Train Accuracy: 0.7589, Val Loss: 0.5684, Val Accuracy: 0.8570 Epoch 130/1000, Train Loss: 0.5547, Train Accuracy: 0.7584, Val Loss: 0.5667, Val Accuracy: 0.8580 Epoch 131/1000, Train Loss: 0.5538, Train Accuracy: 0.7635, Val Loss: 0.5648, Val Accuracy: 0.8594 Epoch 132/1000, Train Loss: 0.5507, Train Accuracy: 0.7610, Val Loss: 0.5631, Val Accuracy: 0.8604 Epoch 133/1000, Train Loss: 0.5504, Train Accuracy: 0.7643, Val Loss: 0.5612, Val Accuracy: 0.8604 Epoch 134/1000, Train Loss: 0.5473, Train Accuracy: 0.7685, Val Loss: 0.5594, Val Accuracy: 0.8624 Epoch 135/1000, Train Loss: 0.5452, Train Accuracy: 0.7689, Val Loss: 0.5575, Val Accuracy: 0.8638 Epoch 136/1000, Train Loss: 0.5448, Train Accuracy: 0.7705, Val Loss: 0.5556, Val Accuracy: 0.8648 Epoch 137/1000, Train Loss: 0.5418, Train Accuracy: 0.7751, Val Loss: 0.5539, Val Accuracy: 0.8658 Epoch 138/1000, Train Loss: 0.5414, Train Accuracy: 0.7741, Val Loss: 0.5519, Val Accuracy: 0.8658 Epoch 139/1000, Train Loss: 0.5397, Train Accuracy: 0.7731, Val Loss: 0.5499, Val Accuracy: 0.8662 Epoch 140/1000, Train Loss: 0.5374, Train Accuracy: 0.7752, Val Loss: 0.5480, Val Accuracy: 0.8667 Epoch 141/1000, Train Loss: 0.5354, Train Accuracy: 0.7796, Val Loss: 0.5460, Val Accuracy: 0.8672 Epoch 142/1000, Train Loss: 0.5338, Train Accuracy: 0.7846, Val Loss: 0.5440, Val Accuracy: 0.8677 Epoch 143/1000, Train Loss: 0.5304, Train Accuracy: 0.7845, Val Loss: 0.5420, Val Accuracy: 0.8672 Epoch 144/1000, Train Loss: 0.5298, Train Accuracy: 0.7851, Val Loss: 0.5401, Val Accuracy: 0.8677 Epoch 145/1000, Train Loss: 0.5270, Train Accuracy: 0.7862, Val Loss: 0.5381, Val Accuracy: 0.8696 Epoch 146/1000, Train Loss: 0.5246, Train Accuracy: 0.7865, Val Loss: 0.5360, Val Accuracy: 0.8701 Epoch 147/1000, Train Loss: 0.5232, Train Accuracy: 0.7954, Val Loss: 0.5339, Val Accuracy: 0.8692 Epoch 148/1000, Train Loss: 0.5210, Train Accuracy: 0.7954, Val Loss: 0.5319, Val Accuracy: 0.8696 Epoch 149/1000, Train Loss: 0.5197, Train Accuracy: 0.7919, Val Loss: 0.5300, Val Accuracy: 0.8701 Epoch 150/1000, Train Loss: 0.5179, Train Accuracy: 0.8009, Val Loss: 0.5280, Val Accuracy: 0.8721 Epoch 151/1000, Train Loss: 0.5161, Train Accuracy: 0.7983, Val Loss: 0.5259, Val Accuracy: 0.8735 Epoch 152/1000, Train Loss: 0.5122, Train Accuracy: 0.8015, Val Loss: 0.5238, Val Accuracy: 0.8735 Epoch 153/1000, Train Loss: 0.5120, Train Accuracy: 0.8036, Val Loss: 0.5217, Val Accuracy: 0.8750 Epoch 154/1000, Train Loss: 0.5108, Train Accuracy: 0.8073, Val Loss: 0.5195, Val Accuracy: 0.8755 Epoch 155/1000, Train Loss: 0.5083, Train Accuracy: 0.8057, Val Loss: 0.5175, Val Accuracy: 0.8755 Epoch 156/1000, Train Loss: 0.5068, Train Accuracy: 0.8023, Val Loss: 0.5155, Val Accuracy: 0.8755 Epoch 157/1000, Train Loss: 0.5019, Train Accuracy: 0.8138, Val Loss: 0.5134, Val Accuracy: 0.8765 Epoch 158/1000, Train Loss: 0.5029, Train Accuracy: 0.8121, Val Loss: 0.5114, Val Accuracy: 0.8784 Epoch 159/1000, Train Loss: 0.5011, Train Accuracy: 0.8116, Val Loss: 0.5093, Val Accuracy: 0.8784 Epoch 160/1000, Train Loss: 0.4989, Train Accuracy: 0.8132, Val Loss: 0.5072, Val Accuracy: 0.8794 Epoch 161/1000, Train Loss: 0.4947, Train Accuracy: 0.8203, Val Loss: 0.5051, Val Accuracy: 0.8799 Epoch 162/1000, Train Loss: 0.4945, Train Accuracy: 0.8140, Val Loss: 0.5030, Val Accuracy: 0.8799 Epoch 163/1000, Train Loss: 0.4924, Train Accuracy: 0.8207, Val Loss: 0.5008, Val Accuracy: 0.8804 Epoch 164/1000, Train Loss: 0.4896, Train Accuracy: 0.8173, Val Loss: 0.4989, Val Accuracy: 0.8804 Epoch 165/1000, Train Loss: 0.4874, Train Accuracy: 0.8280, Val Loss: 0.4966, Val Accuracy: 0.8799 Epoch 166/1000, Train Loss: 0.4834, Train Accuracy: 0.8295, Val Loss: 0.4945, Val Accuracy: 0.8799 Epoch 167/1000, Train Loss: 0.4835, Train Accuracy: 0.8286, Val Loss: 0.4924, Val Accuracy: 0.8808 Epoch 168/1000, Train Loss: 0.4800, Train Accuracy: 0.8308, Val Loss: 0.4903, Val Accuracy: 0.8808 Epoch 169/1000, Train Loss: 0.4783, Train Accuracy: 0.8319, Val Loss: 0.4880, Val Accuracy: 0.8813 Epoch 170/1000, Train Loss: 0.4793, Train Accuracy: 0.8285, Val Loss: 0.4859, Val Accuracy: 0.8823 Epoch 171/1000, Train Loss: 0.4732, Train Accuracy: 0.8343, Val Loss: 0.4837, Val Accuracy: 0.8823 Epoch 172/1000, Train Loss: 0.4740, Train Accuracy: 0.8343, Val Loss: 0.4815, Val Accuracy: 0.8828 Epoch 173/1000, Train Loss: 0.4694, Train Accuracy: 0.8394, Val Loss: 0.4793, Val Accuracy: 0.8847 Epoch 174/1000, Train Loss: 0.4679, Train Accuracy: 0.8417, Val Loss: 0.4771, Val Accuracy: 0.8852 Epoch 175/1000, Train Loss: 0.4673, Train Accuracy: 0.8365, Val Loss: 0.4751, Val Accuracy: 0.8852 Epoch 176/1000, Train Loss: 0.4669, Train Accuracy: 0.8414, Val Loss: 0.4727, Val Accuracy: 0.8872 Epoch 177/1000, Train Loss: 0.4616, Train Accuracy: 0.8490, Val Loss: 0.4706, Val Accuracy: 0.8867 Epoch 178/1000, Train Loss: 0.4617, Train Accuracy: 0.8420, Val Loss: 0.4686, Val Accuracy: 0.8876 Epoch 179/1000, Train Loss: 0.4584, Train Accuracy: 0.8489, Val Loss: 0.4664, Val Accuracy: 0.8872 Epoch 180/1000, Train Loss: 0.4565, Train Accuracy: 0.8477, Val Loss: 0.4642, Val Accuracy: 0.8876 Epoch 181/1000, Train Loss: 0.4523, Train Accuracy: 0.8494, Val Loss: 0.4620, Val Accuracy: 0.8876 Epoch 182/1000, Train Loss: 0.4510, Train Accuracy: 0.8542, Val Loss: 0.4599, Val Accuracy: 0.8881 Epoch 183/1000, Train Loss: 0.4503, Train Accuracy: 0.8512, Val Loss: 0.4578, Val Accuracy: 0.8881 Epoch 184/1000, Train Loss: 0.4490, Train Accuracy: 0.8551, Val Loss: 0.4556, Val Accuracy: 0.8886 Epoch 185/1000, Train Loss: 0.4460, Train Accuracy: 0.8511, Val Loss: 0.4536, Val Accuracy: 0.8886 Epoch 186/1000, Train Loss: 0.4439, Train Accuracy: 0.8589, Val Loss: 0.4515, Val Accuracy: 0.8901 Epoch 187/1000, Train Loss: 0.4409, Train Accuracy: 0.8616, Val Loss: 0.4493, Val Accuracy: 0.8906 Epoch 188/1000, Train Loss: 0.4384, Train Accuracy: 0.8594, Val Loss: 0.4473, Val Accuracy: 0.8901 Epoch 189/1000, Train Loss: 0.4369, Train Accuracy: 0.8587, Val Loss: 0.4451, Val Accuracy: 0.8901 Epoch 190/1000, Train Loss: 0.4381, Train Accuracy: 0.8557, Val Loss: 0.4431, Val Accuracy: 0.8891 Epoch 191/1000, Train Loss: 0.4345, Train Accuracy: 0.8627, Val Loss: 0.4409, Val Accuracy: 0.8881 Epoch 192/1000, Train Loss: 0.4323, Train Accuracy: 0.8649, Val Loss: 0.4387, Val Accuracy: 0.8891 Epoch 193/1000, Train Loss: 0.4282, Train Accuracy: 0.8683, Val Loss: 0.4367, Val Accuracy: 0.8876 Epoch 194/1000, Train Loss: 0.4265, Train Accuracy: 0.8643, Val Loss: 0.4345, Val Accuracy: 0.8876 Epoch 195/1000, Train Loss: 0.4244, Train Accuracy: 0.8669, Val Loss: 0.4325, Val Accuracy: 0.8876 Epoch 196/1000, Train Loss: 0.4240, Train Accuracy: 0.8646, Val Loss: 0.4306, Val Accuracy: 0.8872 Epoch 197/1000, Train Loss: 0.4215, Train Accuracy: 0.8700, Val Loss: 0.4284, Val Accuracy: 0.8876 Epoch 198/1000, Train Loss: 0.4189, Train Accuracy: 0.8673, Val Loss: 0.4263, Val Accuracy: 0.8886 Epoch 199/1000, Train Loss: 0.4182, Train Accuracy: 0.8712, Val Loss: 0.4240, Val Accuracy: 0.8891 Epoch 200/1000, Train Loss: 0.4136, Train Accuracy: 0.8737, Val Loss: 0.4219, Val Accuracy: 0.8896 Epoch 201/1000, Train Loss: 0.4111, Train Accuracy: 0.8742, Val Loss: 0.4199, Val Accuracy: 0.8901 Epoch 202/1000, Train Loss: 0.4101, Train Accuracy: 0.8747, Val Loss: 0.4178, Val Accuracy: 0.8915 Epoch 203/1000, Train Loss: 0.4076, Train Accuracy: 0.8735, Val Loss: 0.4157, Val Accuracy: 0.8915 Epoch 204/1000, Train Loss: 0.4059, Train Accuracy: 0.8790, Val Loss: 0.4137, Val Accuracy: 0.8915 Epoch 205/1000, Train Loss: 0.4050, Train Accuracy: 0.8737, Val Loss: 0.4116, Val Accuracy: 0.8915 Epoch 206/1000, Train Loss: 0.4025, Train Accuracy: 0.8768, Val Loss: 0.4096, Val Accuracy: 0.8930 Epoch 207/1000, Train Loss: 0.4001, Train Accuracy: 0.8797, Val Loss: 0.4074, Val Accuracy: 0.8935 Epoch 208/1000, Train Loss: 0.3987, Train Accuracy: 0.8743, Val Loss: 0.4054, Val Accuracy: 0.8945 Epoch 209/1000, Train Loss: 0.3967, Train Accuracy: 0.8743, Val Loss: 0.4033, Val Accuracy: 0.8945 Epoch 210/1000, Train Loss: 0.3943, Train Accuracy: 0.8818, Val Loss: 0.4014, Val Accuracy: 0.8935 Epoch 211/1000, Train Loss: 0.3920, Train Accuracy: 0.8833, Val Loss: 0.3993, Val Accuracy: 0.8940 Epoch 212/1000, Train Loss: 0.3890, Train Accuracy: 0.8824, Val Loss: 0.3974, Val Accuracy: 0.8935 Epoch 213/1000, Train Loss: 0.3884, Train Accuracy: 0.8831, Val Loss: 0.3953, Val Accuracy: 0.8945 Epoch 214/1000, Train Loss: 0.3841, Train Accuracy: 0.8869, Val Loss: 0.3932, Val Accuracy: 0.8940 Epoch 215/1000, Train Loss: 0.3856, Train Accuracy: 0.8844, Val Loss: 0.3910, Val Accuracy: 0.8940 Epoch 216/1000, Train Loss: 0.3823, Train Accuracy: 0.8836, Val Loss: 0.3889, Val Accuracy: 0.8945 Epoch 217/1000, Train Loss: 0.3813, Train Accuracy: 0.8820, Val Loss: 0.3868, Val Accuracy: 0.8949 Epoch 218/1000, Train Loss: 0.3794, Train Accuracy: 0.8905, Val Loss: 0.3847, Val Accuracy: 0.8945 Epoch 219/1000, Train Loss: 0.3764, Train Accuracy: 0.8872, Val Loss: 0.3827, Val Accuracy: 0.8949 Epoch 220/1000, Train Loss: 0.3739, Train Accuracy: 0.8904, Val Loss: 0.3806, Val Accuracy: 0.8949 Epoch 221/1000, Train Loss: 0.3722, Train Accuracy: 0.8872, Val Loss: 0.3786, Val Accuracy: 0.8949 Epoch 222/1000, Train Loss: 0.3693, Train Accuracy: 0.8874, Val Loss: 0.3764, Val Accuracy: 0.8954 Epoch 223/1000, Train Loss: 0.3667, Train Accuracy: 0.8894, Val Loss: 0.3746, Val Accuracy: 0.8954 Epoch 224/1000, Train Loss: 0.3665, Train Accuracy: 0.8898, Val Loss: 0.3728, Val Accuracy: 0.8954 Epoch 225/1000, Train Loss: 0.3641, Train Accuracy: 0.8916, Val Loss: 0.3710, Val Accuracy: 0.8949 Epoch 226/1000, Train Loss: 0.3637, Train Accuracy: 0.8889, Val Loss: 0.3691, Val Accuracy: 0.8954 Epoch 227/1000, Train Loss: 0.3610, Train Accuracy: 0.8906, Val Loss: 0.3672, Val Accuracy: 0.8954 Epoch 228/1000, Train Loss: 0.3599, Train Accuracy: 0.8917, Val Loss: 0.3654, Val Accuracy: 0.8959 Epoch 229/1000, Train Loss: 0.3583, Train Accuracy: 0.8943, Val Loss: 0.3634, Val Accuracy: 0.8959 Epoch 230/1000, Train Loss: 0.3551, Train Accuracy: 0.8962, Val Loss: 0.3616, Val Accuracy: 0.8954 Epoch 231/1000, Train Loss: 0.3534, Train Accuracy: 0.8921, Val Loss: 0.3598, Val Accuracy: 0.8964 Epoch 232/1000, Train Loss: 0.3524, Train Accuracy: 0.8955, Val Loss: 0.3579, Val Accuracy: 0.8974 Epoch 233/1000, Train Loss: 0.3505, Train Accuracy: 0.8950, Val Loss: 0.3561, Val Accuracy: 0.8974 Epoch 234/1000, Train Loss: 0.3498, Train Accuracy: 0.8939, Val Loss: 0.3542, Val Accuracy: 0.8974 Epoch 235/1000, Train Loss: 0.3474, Train Accuracy: 0.8934, Val Loss: 0.3523, Val Accuracy: 0.8969 Epoch 236/1000, Train Loss: 0.3463, Train Accuracy: 0.8948, Val Loss: 0.3506, Val Accuracy: 0.8974 Epoch 237/1000, Train Loss: 0.3420, Train Accuracy: 0.8956, Val Loss: 0.3488, Val Accuracy: 0.8974 Epoch 238/1000, Train Loss: 0.3409, Train Accuracy: 0.8975, Val Loss: 0.3470, Val Accuracy: 0.8974 Epoch 239/1000, Train Loss: 0.3406, Train Accuracy: 0.8958, Val Loss: 0.3451, Val Accuracy: 0.8979 Epoch 240/1000, Train Loss: 0.3377, Train Accuracy: 0.8990, Val Loss: 0.3433, Val Accuracy: 0.8979 Epoch 241/1000, Train Loss: 0.3367, Train Accuracy: 0.8972, Val Loss: 0.3415, Val Accuracy: 0.8983 Epoch 242/1000, Train Loss: 0.3369, Train Accuracy: 0.8972, Val Loss: 0.3398, Val Accuracy: 0.8979 Epoch 243/1000, Train Loss: 0.3333, Train Accuracy: 0.8988, Val Loss: 0.3380, Val Accuracy: 0.8979 Epoch 244/1000, Train Loss: 0.3313, Train Accuracy: 0.8973, Val Loss: 0.3363, Val Accuracy: 0.8974 Epoch 245/1000, Train Loss: 0.3300, Train Accuracy: 0.8971, Val Loss: 0.3347, Val Accuracy: 0.8979 Epoch 246/1000, Train Loss: 0.3291, Train Accuracy: 0.8978, Val Loss: 0.3330, Val Accuracy: 0.8983 Epoch 247/1000, Train Loss: 0.3249, Train Accuracy: 0.9034, Val Loss: 0.3315, Val Accuracy: 0.8983 Epoch 248/1000, Train Loss: 0.3237, Train Accuracy: 0.8990, Val Loss: 0.3299, Val Accuracy: 0.8983 Epoch 249/1000, Train Loss: 0.3206, Train Accuracy: 0.9055, Val Loss: 0.3280, Val Accuracy: 0.8993 Epoch 250/1000, Train Loss: 0.3233, Train Accuracy: 0.9020, Val Loss: 0.3264, Val Accuracy: 0.8993 Epoch 251/1000, Train Loss: 0.3192, Train Accuracy: 0.9040, Val Loss: 0.3247, Val Accuracy: 0.9008 Epoch 252/1000, Train Loss: 0.3186, Train Accuracy: 0.9024, Val Loss: 0.3229, Val Accuracy: 0.9018 Epoch 253/1000, Train Loss: 0.3169, Train Accuracy: 0.9033, Val Loss: 0.3215, Val Accuracy: 0.9008 Epoch 254/1000, Train Loss: 0.3155, Train Accuracy: 0.9057, Val Loss: 0.3199, Val Accuracy: 0.9013 Epoch 255/1000, Train Loss: 0.3127, Train Accuracy: 0.9074, Val Loss: 0.3183, Val Accuracy: 0.9013 Epoch 256/1000, Train Loss: 0.3118, Train Accuracy: 0.9045, Val Loss: 0.3168, Val Accuracy: 0.9013 Epoch 257/1000, Train Loss: 0.3105, Train Accuracy: 0.9041, Val Loss: 0.3151, Val Accuracy: 0.9018 Epoch 258/1000, Train Loss: 0.3109, Train Accuracy: 0.9040, Val Loss: 0.3136, Val Accuracy: 0.9013 Epoch 259/1000, Train Loss: 0.3033, Train Accuracy: 0.9082, Val Loss: 0.3122, Val Accuracy: 0.9013 Epoch 260/1000, Train Loss: 0.3078, Train Accuracy: 0.9050, Val Loss: 0.3107, Val Accuracy: 0.9013 Epoch 261/1000, Train Loss: 0.3042, Train Accuracy: 0.9056, Val Loss: 0.3092, Val Accuracy: 0.9018 Epoch 262/1000, Train Loss: 0.3017, Train Accuracy: 0.9085, Val Loss: 0.3078, Val Accuracy: 0.9008 Epoch 263/1000, Train Loss: 0.3024, Train Accuracy: 0.9063, Val Loss: 0.3062, Val Accuracy: 0.9013 Epoch 264/1000, Train Loss: 0.2986, Train Accuracy: 0.9083, Val Loss: 0.3047, Val Accuracy: 0.9013 Epoch 265/1000, Train Loss: 0.2989, Train Accuracy: 0.9056, Val Loss: 0.3032, Val Accuracy: 0.9018 Epoch 266/1000, Train Loss: 0.2971, Train Accuracy: 0.9062, Val Loss: 0.3019, Val Accuracy: 0.9008 Epoch 267/1000, Train Loss: 0.2931, Train Accuracy: 0.9091, Val Loss: 0.3006, Val Accuracy: 0.9013 Epoch 268/1000, Train Loss: 0.2947, Train Accuracy: 0.9101, Val Loss: 0.2993, Val Accuracy: 0.9013 Epoch 269/1000, Train Loss: 0.2908, Train Accuracy: 0.9108, Val Loss: 0.2977, Val Accuracy: 0.9013 Epoch 270/1000, Train Loss: 0.2930, Train Accuracy: 0.9069, Val Loss: 0.2962, Val Accuracy: 0.9022 Epoch 271/1000, Train Loss: 0.2881, Train Accuracy: 0.9093, Val Loss: 0.2949, Val Accuracy: 0.9022 Epoch 272/1000, Train Loss: 0.2895, Train Accuracy: 0.9088, Val Loss: 0.2935, Val Accuracy: 0.9022 Epoch 273/1000, Train Loss: 0.2859, Train Accuracy: 0.9105, Val Loss: 0.2921, Val Accuracy: 0.9032 Epoch 274/1000, Train Loss: 0.2851, Train Accuracy: 0.9139, Val Loss: 0.2908, Val Accuracy: 0.9032 Epoch 275/1000, Train Loss: 0.2837, Train Accuracy: 0.9114, Val Loss: 0.2897, Val Accuracy: 0.9032 Epoch 276/1000, Train Loss: 0.2813, Train Accuracy: 0.9118, Val Loss: 0.2883, Val Accuracy: 0.9032 Epoch 277/1000, Train Loss: 0.2817, Train Accuracy: 0.9102, Val Loss: 0.2870, Val Accuracy: 0.9042 Epoch 278/1000, Train Loss: 0.2778, Train Accuracy: 0.9135, Val Loss: 0.2855, Val Accuracy: 0.9042 Epoch 279/1000, Train Loss: 0.2764, Train Accuracy: 0.9138, Val Loss: 0.2840, Val Accuracy: 0.9047 Epoch 280/1000, Train Loss: 0.2787, Train Accuracy: 0.9106, Val Loss: 0.2829, Val Accuracy: 0.9047 Epoch 281/1000, Train Loss: 0.2741, Train Accuracy: 0.9100, Val Loss: 0.2815, Val Accuracy: 0.9052 Epoch 282/1000, Train Loss: 0.2742, Train Accuracy: 0.9141, Val Loss: 0.2803, Val Accuracy: 0.9052 Epoch 283/1000, Train Loss: 0.2750, Train Accuracy: 0.9123, Val Loss: 0.2791, Val Accuracy: 0.9056 Epoch 284/1000, Train Loss: 0.2734, Train Accuracy: 0.9150, Val Loss: 0.2779, Val Accuracy: 0.9056 Epoch 285/1000, Train Loss: 0.2718, Train Accuracy: 0.9141, Val Loss: 0.2766, Val Accuracy: 0.9061 Epoch 286/1000, Train Loss: 0.2705, Train Accuracy: 0.9139, Val Loss: 0.2756, Val Accuracy: 0.9056 Epoch 287/1000, Train Loss: 0.2699, Train Accuracy: 0.9140, Val Loss: 0.2743, Val Accuracy: 0.9056 Epoch 288/1000, Train Loss: 0.2669, Train Accuracy: 0.9172, Val Loss: 0.2732, Val Accuracy: 0.9056 Epoch 289/1000, Train Loss: 0.2665, Train Accuracy: 0.9150, Val Loss: 0.2720, Val Accuracy: 0.9061 Epoch 290/1000, Train Loss: 0.2664, Train Accuracy: 0.9162, Val Loss: 0.2709, Val Accuracy: 0.9066 Epoch 291/1000, Train Loss: 0.2663, Train Accuracy: 0.9149, Val Loss: 0.2698, Val Accuracy: 0.9071 Epoch 292/1000, Train Loss: 0.2628, Train Accuracy: 0.9197, Val Loss: 0.2686, Val Accuracy: 0.9066 Epoch 293/1000, Train Loss: 0.2630, Train Accuracy: 0.9147, Val Loss: 0.2674, Val Accuracy: 0.9071 Epoch 294/1000, Train Loss: 0.2606, Train Accuracy: 0.9149, Val Loss: 0.2663, Val Accuracy: 0.9076 Epoch 295/1000, Train Loss: 0.2591, Train Accuracy: 0.9195, Val Loss: 0.2651, Val Accuracy: 0.9076 Epoch 296/1000, Train Loss: 0.2597, Train Accuracy: 0.9136, Val Loss: 0.2639, Val Accuracy: 0.9076 Epoch 297/1000, Train Loss: 0.2577, Train Accuracy: 0.9180, Val Loss: 0.2627, Val Accuracy: 0.9081 Epoch 298/1000, Train Loss: 0.2582, Train Accuracy: 0.9152, Val Loss: 0.2615, Val Accuracy: 0.9081 Epoch 299/1000, Train Loss: 0.2576, Train Accuracy: 0.9145, Val Loss: 0.2604, Val Accuracy: 0.9081 Epoch 300/1000, Train Loss: 0.2532, Train Accuracy: 0.9170, Val Loss: 0.2594, Val Accuracy: 0.9086 Epoch 301/1000, Train Loss: 0.2501, Train Accuracy: 0.9187, Val Loss: 0.2583, Val Accuracy: 0.9086 Epoch 302/1000, Train Loss: 0.2513, Train Accuracy: 0.9173, Val Loss: 0.2575, Val Accuracy: 0.9086 Epoch 303/1000, Train Loss: 0.2495, Train Accuracy: 0.9225, Val Loss: 0.2565, Val Accuracy: 0.9086 Epoch 304/1000, Train Loss: 0.2502, Train Accuracy: 0.9162, Val Loss: 0.2555, Val Accuracy: 0.9090 Epoch 305/1000, Train Loss: 0.2491, Train Accuracy: 0.9200, Val Loss: 0.2547, Val Accuracy: 0.9105 Epoch 306/1000, Train Loss: 0.2493, Train Accuracy: 0.9200, Val Loss: 0.2539, Val Accuracy: 0.9100 Epoch 307/1000, Train Loss: 0.2479, Train Accuracy: 0.9187, Val Loss: 0.2529, Val Accuracy: 0.9100 Epoch 308/1000, Train Loss: 0.2453, Train Accuracy: 0.9212, Val Loss: 0.2519, Val Accuracy: 0.9100 Epoch 309/1000, Train Loss: 0.2422, Train Accuracy: 0.9212, Val Loss: 0.2510, Val Accuracy: 0.9100 Epoch 310/1000, Train Loss: 0.2446, Train Accuracy: 0.9214, Val Loss: 0.2499, Val Accuracy: 0.9105 Epoch 311/1000, Train Loss: 0.2438, Train Accuracy: 0.9207, Val Loss: 0.2489, Val Accuracy: 0.9110 Epoch 312/1000, Train Loss: 0.2450, Train Accuracy: 0.9178, Val Loss: 0.2479, Val Accuracy: 0.9110 Epoch 313/1000, Train Loss: 0.2378, Train Accuracy: 0.9230, Val Loss: 0.2470, Val Accuracy: 0.9115 Epoch 314/1000, Train Loss: 0.2389, Train Accuracy: 0.9228, Val Loss: 0.2461, Val Accuracy: 0.9115 Epoch 315/1000, Train Loss: 0.2403, Train Accuracy: 0.9189, Val Loss: 0.2452, Val Accuracy: 0.9115 Epoch 316/1000, Train Loss: 0.2372, Train Accuracy: 0.9225, Val Loss: 0.2441, Val Accuracy: 0.9115 Epoch 317/1000, Train Loss: 0.2371, Train Accuracy: 0.9231, Val Loss: 0.2433, Val Accuracy: 0.9115 Epoch 318/1000, Train Loss: 0.2397, Train Accuracy: 0.9200, Val Loss: 0.2424, Val Accuracy: 0.9115 Epoch 319/1000, Train Loss: 0.2358, Train Accuracy: 0.9202, Val Loss: 0.2415, Val Accuracy: 0.9120 Epoch 320/1000, Train Loss: 0.2325, Train Accuracy: 0.9247, Val Loss: 0.2405, Val Accuracy: 0.9129 Epoch 321/1000, Train Loss: 0.2355, Train Accuracy: 0.9232, Val Loss: 0.2400, Val Accuracy: 0.9134 Epoch 322/1000, Train Loss: 0.2321, Train Accuracy: 0.9259, Val Loss: 0.2392, Val Accuracy: 0.9125 Epoch 323/1000, Train Loss: 0.2327, Train Accuracy: 0.9226, Val Loss: 0.2382, Val Accuracy: 0.9134 Epoch 324/1000, Train Loss: 0.2333, Train Accuracy: 0.9263, Val Loss: 0.2373, Val Accuracy: 0.9134 Epoch 325/1000, Train Loss: 0.2280, Train Accuracy: 0.9265, Val Loss: 0.2362, Val Accuracy: 0.9149 Epoch 326/1000, Train Loss: 0.2311, Train Accuracy: 0.9218, Val Loss: 0.2352, Val Accuracy: 0.9159 Epoch 327/1000, Train Loss: 0.2305, Train Accuracy: 0.9218, Val Loss: 0.2346, Val Accuracy: 0.9159 Epoch 328/1000, Train Loss: 0.2278, Train Accuracy: 0.9243, Val Loss: 0.2339, Val Accuracy: 0.9149 Epoch 329/1000, Train Loss: 0.2258, Train Accuracy: 0.9256, Val Loss: 0.2334, Val Accuracy: 0.9144 Epoch 330/1000, Train Loss: 0.2252, Train Accuracy: 0.9269, Val Loss: 0.2327, Val Accuracy: 0.9139 Epoch 331/1000, Train Loss: 0.2237, Train Accuracy: 0.9259, Val Loss: 0.2316, Val Accuracy: 0.9144 Epoch 332/1000, Train Loss: 0.2240, Train Accuracy: 0.9271, Val Loss: 0.2307, Val Accuracy: 0.9144 Epoch 333/1000, Train Loss: 0.2224, Train Accuracy: 0.9246, Val Loss: 0.2300, Val Accuracy: 0.9149 Epoch 334/1000, Train Loss: 0.2212, Train Accuracy: 0.9282, Val Loss: 0.2293, Val Accuracy: 0.9149 Epoch 335/1000, Train Loss: 0.2220, Train Accuracy: 0.9274, Val Loss: 0.2286, Val Accuracy: 0.9154 Epoch 336/1000, Train Loss: 0.2210, Train Accuracy: 0.9260, Val Loss: 0.2278, Val Accuracy: 0.9159 Epoch 337/1000, Train Loss: 0.2207, Train Accuracy: 0.9260, Val Loss: 0.2273, Val Accuracy: 0.9159 Epoch 338/1000, Train Loss: 0.2204, Train Accuracy: 0.9275, Val Loss: 0.2268, Val Accuracy: 0.9154 Epoch 339/1000, Train Loss: 0.2178, Train Accuracy: 0.9274, Val Loss: 0.2261, Val Accuracy: 0.9168 Epoch 340/1000, Train Loss: 0.2191, Train Accuracy: 0.9260, Val Loss: 0.2254, Val Accuracy: 0.9168 Epoch 341/1000, Train Loss: 0.2173, Train Accuracy: 0.9299, Val Loss: 0.2245, Val Accuracy: 0.9178 Epoch 342/1000, Train Loss: 0.2166, Train Accuracy: 0.9242, Val Loss: 0.2238, Val Accuracy: 0.9183 Epoch 343/1000, Train Loss: 0.2160, Train Accuracy: 0.9271, Val Loss: 0.2230, Val Accuracy: 0.9188 Epoch 344/1000, Train Loss: 0.2146, Train Accuracy: 0.9285, Val Loss: 0.2222, Val Accuracy: 0.9188 Epoch 345/1000, Train Loss: 0.2159, Train Accuracy: 0.9240, Val Loss: 0.2216, Val Accuracy: 0.9188 Epoch 346/1000, Train Loss: 0.2110, Train Accuracy: 0.9299, Val Loss: 0.2209, Val Accuracy: 0.9188 Epoch 347/1000, Train Loss: 0.2141, Train Accuracy: 0.9254, Val Loss: 0.2204, Val Accuracy: 0.9188 Epoch 348/1000, Train Loss: 0.2127, Train Accuracy: 0.9277, Val Loss: 0.2199, Val Accuracy: 0.9188 Epoch 349/1000, Train Loss: 0.2061, Train Accuracy: 0.9288, Val Loss: 0.2190, Val Accuracy: 0.9188 Epoch 350/1000, Train Loss: 0.2106, Train Accuracy: 0.9275, Val Loss: 0.2183, Val Accuracy: 0.9193 Epoch 351/1000, Train Loss: 0.2081, Train Accuracy: 0.9296, Val Loss: 0.2176, Val Accuracy: 0.9197 Epoch 352/1000, Train Loss: 0.2090, Train Accuracy: 0.9269, Val Loss: 0.2170, Val Accuracy: 0.9193 Epoch 353/1000, Train Loss: 0.2105, Train Accuracy: 0.9285, Val Loss: 0.2163, Val Accuracy: 0.9193 Epoch 354/1000, Train Loss: 0.2092, Train Accuracy: 0.9274, Val Loss: 0.2159, Val Accuracy: 0.9193 Epoch 355/1000, Train Loss: 0.2056, Train Accuracy: 0.9302, Val Loss: 0.2150, Val Accuracy: 0.9197 Epoch 356/1000, Train Loss: 0.2052, Train Accuracy: 0.9299, Val Loss: 0.2142, Val Accuracy: 0.9212 Epoch 357/1000, Train Loss: 0.2047, Train Accuracy: 0.9312, Val Loss: 0.2137, Val Accuracy: 0.9212 Epoch 358/1000, Train Loss: 0.2010, Train Accuracy: 0.9326, Val Loss: 0.2131, Val Accuracy: 0.9212 Epoch 359/1000, Train Loss: 0.2023, Train Accuracy: 0.9322, Val Loss: 0.2124, Val Accuracy: 0.9217 Epoch 360/1000, Train Loss: 0.2015, Train Accuracy: 0.9302, Val Loss: 0.2117, Val Accuracy: 0.9227 Epoch 361/1000, Train Loss: 0.2058, Train Accuracy: 0.9279, Val Loss: 0.2114, Val Accuracy: 0.9227 Epoch 362/1000, Train Loss: 0.2038, Train Accuracy: 0.9325, Val Loss: 0.2109, Val Accuracy: 0.9232 Epoch 363/1000, Train Loss: 0.1990, Train Accuracy: 0.9326, Val Loss: 0.2104, Val Accuracy: 0.9232 Epoch 364/1000, Train Loss: 0.1990, Train Accuracy: 0.9324, Val Loss: 0.2098, Val Accuracy: 0.9232 Epoch 365/1000, Train Loss: 0.2017, Train Accuracy: 0.9301, Val Loss: 0.2092, Val Accuracy: 0.9241 Epoch 366/1000, Train Loss: 0.1983, Train Accuracy: 0.9329, Val Loss: 0.2085, Val Accuracy: 0.9241 Epoch 367/1000, Train Loss: 0.1976, Train Accuracy: 0.9339, Val Loss: 0.2079, Val Accuracy: 0.9241 Epoch 368/1000, Train Loss: 0.1991, Train Accuracy: 0.9332, Val Loss: 0.2073, Val Accuracy: 0.9241 Epoch 369/1000, Train Loss: 0.1970, Train Accuracy: 0.9293, Val Loss: 0.2068, Val Accuracy: 0.9241 Epoch 370/1000, Train Loss: 0.1973, Train Accuracy: 0.9318, Val Loss: 0.2063, Val Accuracy: 0.9246 Epoch 371/1000, Train Loss: 0.1967, Train Accuracy: 0.9326, Val Loss: 0.2058, Val Accuracy: 0.9246 Epoch 372/1000, Train Loss: 0.1956, Train Accuracy: 0.9352, Val Loss: 0.2052, Val Accuracy: 0.9251 Epoch 373/1000, Train Loss: 0.1961, Train Accuracy: 0.9308, Val Loss: 0.2048, Val Accuracy: 0.9261 Epoch 374/1000, Train Loss: 0.1948, Train Accuracy: 0.9336, Val Loss: 0.2040, Val Accuracy: 0.9261 Epoch 375/1000, Train Loss: 0.1966, Train Accuracy: 0.9330, Val Loss: 0.2034, Val Accuracy: 0.9266 Epoch 376/1000, Train Loss: 0.1930, Train Accuracy: 0.9361, Val Loss: 0.2029, Val Accuracy: 0.9261 Epoch 377/1000, Train Loss: 0.1950, Train Accuracy: 0.9349, Val Loss: 0.2024, Val Accuracy: 0.9261 Epoch 378/1000, Train Loss: 0.1926, Train Accuracy: 0.9325, Val Loss: 0.2019, Val Accuracy: 0.9266 Epoch 379/1000, Train Loss: 0.1919, Train Accuracy: 0.9350, Val Loss: 0.2014, Val Accuracy: 0.9266 Epoch 380/1000, Train Loss: 0.1895, Train Accuracy: 0.9361, Val Loss: 0.2009, Val Accuracy: 0.9266 Epoch 381/1000, Train Loss: 0.1917, Train Accuracy: 0.9348, Val Loss: 0.2005, Val Accuracy: 0.9270 Epoch 382/1000, Train Loss: 0.1905, Train Accuracy: 0.9354, Val Loss: 0.2000, Val Accuracy: 0.9270 Epoch 383/1000, Train Loss: 0.1868, Train Accuracy: 0.9371, Val Loss: 0.1994, Val Accuracy: 0.9275 Epoch 384/1000, Train Loss: 0.1892, Train Accuracy: 0.9367, Val Loss: 0.1988, Val Accuracy: 0.9266 Epoch 385/1000, Train Loss: 0.1890, Train Accuracy: 0.9369, Val Loss: 0.1984, Val Accuracy: 0.9266 Epoch 386/1000, Train Loss: 0.1886, Train Accuracy: 0.9381, Val Loss: 0.1978, Val Accuracy: 0.9266 Epoch 387/1000, Train Loss: 0.1899, Train Accuracy: 0.9343, Val Loss: 0.1974, Val Accuracy: 0.9275 Epoch 388/1000, Train Loss: 0.1868, Train Accuracy: 0.9357, Val Loss: 0.1970, Val Accuracy: 0.9280 Epoch 389/1000, Train Loss: 0.1843, Train Accuracy: 0.9371, Val Loss: 0.1965, Val Accuracy: 0.9280 Epoch 390/1000, Train Loss: 0.1835, Train Accuracy: 0.9366, Val Loss: 0.1960, Val Accuracy: 0.9285 Epoch 391/1000, Train Loss: 0.1845, Train Accuracy: 0.9380, Val Loss: 0.1956, Val Accuracy: 0.9295 Epoch 392/1000, Train Loss: 0.1852, Train Accuracy: 0.9358, Val Loss: 0.1952, Val Accuracy: 0.9290 Epoch 393/1000, Train Loss: 0.1820, Train Accuracy: 0.9380, Val Loss: 0.1947, Val Accuracy: 0.9290 Epoch 394/1000, Train Loss: 0.1809, Train Accuracy: 0.9387, Val Loss: 0.1942, Val Accuracy: 0.9290 Epoch 395/1000, Train Loss: 0.1831, Train Accuracy: 0.9378, Val Loss: 0.1941, Val Accuracy: 0.9280 Epoch 396/1000, Train Loss: 0.1837, Train Accuracy: 0.9378, Val Loss: 0.1934, Val Accuracy: 0.9295 Epoch 397/1000, Train Loss: 0.1802, Train Accuracy: 0.9405, Val Loss: 0.1930, Val Accuracy: 0.9295 Epoch 398/1000, Train Loss: 0.1827, Train Accuracy: 0.9392, Val Loss: 0.1924, Val Accuracy: 0.9295 Epoch 399/1000, Train Loss: 0.1790, Train Accuracy: 0.9359, Val Loss: 0.1919, Val Accuracy: 0.9295 Epoch 400/1000, Train Loss: 0.1799, Train Accuracy: 0.9380, Val Loss: 0.1915, Val Accuracy: 0.9295 Epoch 401/1000, Train Loss: 0.1780, Train Accuracy: 0.9400, Val Loss: 0.1912, Val Accuracy: 0.9295 Epoch 402/1000, Train Loss: 0.1760, Train Accuracy: 0.9398, Val Loss: 0.1908, Val Accuracy: 0.9290 Epoch 403/1000, Train Loss: 0.1792, Train Accuracy: 0.9378, Val Loss: 0.1904, Val Accuracy: 0.9290 Epoch 404/1000, Train Loss: 0.1771, Train Accuracy: 0.9405, Val Loss: 0.1899, Val Accuracy: 0.9295 Epoch 405/1000, Train Loss: 0.1758, Train Accuracy: 0.9394, Val Loss: 0.1893, Val Accuracy: 0.9295 Epoch 406/1000, Train Loss: 0.1766, Train Accuracy: 0.9406, Val Loss: 0.1888, Val Accuracy: 0.9295 Epoch 407/1000, Train Loss: 0.1742, Train Accuracy: 0.9404, Val Loss: 0.1886, Val Accuracy: 0.9295 Epoch 408/1000, Train Loss: 0.1749, Train Accuracy: 0.9412, Val Loss: 0.1882, Val Accuracy: 0.9295 Epoch 409/1000, Train Loss: 0.1773, Train Accuracy: 0.9376, Val Loss: 0.1878, Val Accuracy: 0.9295 Epoch 410/1000, Train Loss: 0.1762, Train Accuracy: 0.9388, Val Loss: 0.1874, Val Accuracy: 0.9295 Epoch 411/1000, Train Loss: 0.1752, Train Accuracy: 0.9397, Val Loss: 0.1871, Val Accuracy: 0.9300 Epoch 412/1000, Train Loss: 0.1751, Train Accuracy: 0.9394, Val Loss: 0.1868, Val Accuracy: 0.9304 Epoch 413/1000, Train Loss: 0.1734, Train Accuracy: 0.9412, Val Loss: 0.1864, Val Accuracy: 0.9304 Epoch 414/1000, Train Loss: 0.1700, Train Accuracy: 0.9421, Val Loss: 0.1858, Val Accuracy: 0.9304 Epoch 415/1000, Train Loss: 0.1714, Train Accuracy: 0.9408, Val Loss: 0.1852, Val Accuracy: 0.9300 Epoch 416/1000, Train Loss: 0.1755, Train Accuracy: 0.9380, Val Loss: 0.1848, Val Accuracy: 0.9300 Epoch 417/1000, Train Loss: 0.1718, Train Accuracy: 0.9393, Val Loss: 0.1844, Val Accuracy: 0.9300 Epoch 418/1000, Train Loss: 0.1695, Train Accuracy: 0.9412, Val Loss: 0.1841, Val Accuracy: 0.9300 Epoch 419/1000, Train Loss: 0.1713, Train Accuracy: 0.9381, Val Loss: 0.1837, Val Accuracy: 0.9300 Epoch 420/1000, Train Loss: 0.1706, Train Accuracy: 0.9417, Val Loss: 0.1834, Val Accuracy: 0.9300 Epoch 421/1000, Train Loss: 0.1722, Train Accuracy: 0.9394, Val Loss: 0.1832, Val Accuracy: 0.9304 Epoch 422/1000, Train Loss: 0.1699, Train Accuracy: 0.9420, Val Loss: 0.1829, Val Accuracy: 0.9300 Epoch 423/1000, Train Loss: 0.1657, Train Accuracy: 0.9420, Val Loss: 0.1825, Val Accuracy: 0.9309 Epoch 424/1000, Train Loss: 0.1688, Train Accuracy: 0.9419, Val Loss: 0.1819, Val Accuracy: 0.9309 Epoch 425/1000, Train Loss: 0.1661, Train Accuracy: 0.9420, Val Loss: 0.1817, Val Accuracy: 0.9314 Epoch 426/1000, Train Loss: 0.1678, Train Accuracy: 0.9415, Val Loss: 0.1814, Val Accuracy: 0.9309 Epoch 427/1000, Train Loss: 0.1659, Train Accuracy: 0.9440, Val Loss: 0.1812, Val Accuracy: 0.9309 Epoch 428/1000, Train Loss: 0.1638, Train Accuracy: 0.9430, Val Loss: 0.1811, Val Accuracy: 0.9304 Epoch 429/1000, Train Loss: 0.1660, Train Accuracy: 0.9445, Val Loss: 0.1805, Val Accuracy: 0.9309 Epoch 430/1000, Train Loss: 0.1677, Train Accuracy: 0.9402, Val Loss: 0.1801, Val Accuracy: 0.9314 Epoch 431/1000, Train Loss: 0.1664, Train Accuracy: 0.9412, Val Loss: 0.1795, Val Accuracy: 0.9314 Epoch 432/1000, Train Loss: 0.1672, Train Accuracy: 0.9392, Val Loss: 0.1791, Val Accuracy: 0.9314 Epoch 433/1000, Train Loss: 0.1658, Train Accuracy: 0.9436, Val Loss: 0.1786, Val Accuracy: 0.9319 Epoch 434/1000, Train Loss: 0.1617, Train Accuracy: 0.9444, Val Loss: 0.1783, Val Accuracy: 0.9314 Epoch 435/1000, Train Loss: 0.1625, Train Accuracy: 0.9454, Val Loss: 0.1781, Val Accuracy: 0.9314 Epoch 436/1000, Train Loss: 0.1645, Train Accuracy: 0.9448, Val Loss: 0.1779, Val Accuracy: 0.9319 Epoch 437/1000, Train Loss: 0.1640, Train Accuracy: 0.9425, Val Loss: 0.1776, Val Accuracy: 0.9319 Epoch 438/1000, Train Loss: 0.1639, Train Accuracy: 0.9430, Val Loss: 0.1773, Val Accuracy: 0.9319 Epoch 439/1000, Train Loss: 0.1616, Train Accuracy: 0.9451, Val Loss: 0.1769, Val Accuracy: 0.9319 Epoch 440/1000, Train Loss: 0.1614, Train Accuracy: 0.9436, Val Loss: 0.1766, Val Accuracy: 0.9319 Epoch 441/1000, Train Loss: 0.1616, Train Accuracy: 0.9459, Val Loss: 0.1764, Val Accuracy: 0.9324 Epoch 442/1000, Train Loss: 0.1589, Train Accuracy: 0.9460, Val Loss: 0.1762, Val Accuracy: 0.9329 Epoch 443/1000, Train Loss: 0.1622, Train Accuracy: 0.9431, Val Loss: 0.1757, Val Accuracy: 0.9329 Epoch 444/1000, Train Loss: 0.1592, Train Accuracy: 0.9449, Val Loss: 0.1754, Val Accuracy: 0.9334 Epoch 445/1000, Train Loss: 0.1604, Train Accuracy: 0.9453, Val Loss: 0.1752, Val Accuracy: 0.9334 Epoch 446/1000, Train Loss: 0.1582, Train Accuracy: 0.9460, Val Loss: 0.1750, Val Accuracy: 0.9334 Epoch 447/1000, Train Loss: 0.1577, Train Accuracy: 0.9440, Val Loss: 0.1747, Val Accuracy: 0.9339 Epoch 448/1000, Train Loss: 0.1558, Train Accuracy: 0.9468, Val Loss: 0.1742, Val Accuracy: 0.9339 Epoch 449/1000, Train Loss: 0.1549, Train Accuracy: 0.9476, Val Loss: 0.1739, Val Accuracy: 0.9343 Epoch 450/1000, Train Loss: 0.1580, Train Accuracy: 0.9455, Val Loss: 0.1737, Val Accuracy: 0.9339 Epoch 451/1000, Train Loss: 0.1553, Train Accuracy: 0.9467, Val Loss: 0.1735, Val Accuracy: 0.9339 Epoch 452/1000, Train Loss: 0.1541, Train Accuracy: 0.9483, Val Loss: 0.1731, Val Accuracy: 0.9343 Epoch 453/1000, Train Loss: 0.1562, Train Accuracy: 0.9476, Val Loss: 0.1726, Val Accuracy: 0.9348 Epoch 454/1000, Train Loss: 0.1538, Train Accuracy: 0.9488, Val Loss: 0.1724, Val Accuracy: 0.9343 Epoch 455/1000, Train Loss: 0.1539, Train Accuracy: 0.9466, Val Loss: 0.1722, Val Accuracy: 0.9348 Epoch 456/1000, Train Loss: 0.1548, Train Accuracy: 0.9455, Val Loss: 0.1719, Val Accuracy: 0.9348 Epoch 457/1000, Train Loss: 0.1549, Train Accuracy: 0.9447, Val Loss: 0.1717, Val Accuracy: 0.9348 Epoch 458/1000, Train Loss: 0.1549, Train Accuracy: 0.9445, Val Loss: 0.1713, Val Accuracy: 0.9353 Epoch 459/1000, Train Loss: 0.1542, Train Accuracy: 0.9464, Val Loss: 0.1710, Val Accuracy: 0.9353 Epoch 460/1000, Train Loss: 0.1498, Train Accuracy: 0.9499, Val Loss: 0.1706, Val Accuracy: 0.9348 Epoch 461/1000, Train Loss: 0.1520, Train Accuracy: 0.9489, Val Loss: 0.1703, Val Accuracy: 0.9348 Epoch 462/1000, Train Loss: 0.1551, Train Accuracy: 0.9460, Val Loss: 0.1700, Val Accuracy: 0.9348 Epoch 463/1000, Train Loss: 0.1519, Train Accuracy: 0.9487, Val Loss: 0.1698, Val Accuracy: 0.9348 Epoch 464/1000, Train Loss: 0.1519, Train Accuracy: 0.9462, Val Loss: 0.1695, Val Accuracy: 0.9363 Epoch 465/1000, Train Loss: 0.1523, Train Accuracy: 0.9482, Val Loss: 0.1694, Val Accuracy: 0.9363 Epoch 466/1000, Train Loss: 0.1520, Train Accuracy: 0.9482, Val Loss: 0.1692, Val Accuracy: 0.9363 Epoch 467/1000, Train Loss: 0.1493, Train Accuracy: 0.9482, Val Loss: 0.1690, Val Accuracy: 0.9363 Epoch 468/1000, Train Loss: 0.1478, Train Accuracy: 0.9502, Val Loss: 0.1687, Val Accuracy: 0.9363 Epoch 469/1000, Train Loss: 0.1495, Train Accuracy: 0.9495, Val Loss: 0.1685, Val Accuracy: 0.9363 Epoch 470/1000, Train Loss: 0.1509, Train Accuracy: 0.9470, Val Loss: 0.1681, Val Accuracy: 0.9363 Epoch 471/1000, Train Loss: 0.1500, Train Accuracy: 0.9473, Val Loss: 0.1678, Val Accuracy: 0.9358 Epoch 472/1000, Train Loss: 0.1465, Train Accuracy: 0.9493, Val Loss: 0.1673, Val Accuracy: 0.9358 Epoch 473/1000, Train Loss: 0.1466, Train Accuracy: 0.9498, Val Loss: 0.1671, Val Accuracy: 0.9358 Epoch 474/1000, Train Loss: 0.1479, Train Accuracy: 0.9499, Val Loss: 0.1669, Val Accuracy: 0.9358 Epoch 475/1000, Train Loss: 0.1449, Train Accuracy: 0.9488, Val Loss: 0.1664, Val Accuracy: 0.9358 Epoch 476/1000, Train Loss: 0.1488, Train Accuracy: 0.9465, Val Loss: 0.1661, Val Accuracy: 0.9358 Epoch 477/1000, Train Loss: 0.1473, Train Accuracy: 0.9485, Val Loss: 0.1658, Val Accuracy: 0.9358 Epoch 478/1000, Train Loss: 0.1466, Train Accuracy: 0.9499, Val Loss: 0.1655, Val Accuracy: 0.9353 Epoch 479/1000, Train Loss: 0.1444, Train Accuracy: 0.9511, Val Loss: 0.1653, Val Accuracy: 0.9353 Epoch 480/1000, Train Loss: 0.1470, Train Accuracy: 0.9492, Val Loss: 0.1652, Val Accuracy: 0.9353 Epoch 481/1000, Train Loss: 0.1453, Train Accuracy: 0.9513, Val Loss: 0.1651, Val Accuracy: 0.9358 Epoch 482/1000, Train Loss: 0.1459, Train Accuracy: 0.9504, Val Loss: 0.1643, Val Accuracy: 0.9353 Epoch 483/1000, Train Loss: 0.1457, Train Accuracy: 0.9493, Val Loss: 0.1644, Val Accuracy: 0.9353 Epoch 484/1000, Train Loss: 0.1454, Train Accuracy: 0.9477, Val Loss: 0.1641, Val Accuracy: 0.9353 Epoch 485/1000, Train Loss: 0.1465, Train Accuracy: 0.9495, Val Loss: 0.1638, Val Accuracy: 0.9353 Epoch 486/1000, Train Loss: 0.1428, Train Accuracy: 0.9505, Val Loss: 0.1636, Val Accuracy: 0.9353 Epoch 487/1000, Train Loss: 0.1418, Train Accuracy: 0.9515, Val Loss: 0.1634, Val Accuracy: 0.9353 Epoch 488/1000, Train Loss: 0.1447, Train Accuracy: 0.9496, Val Loss: 0.1631, Val Accuracy: 0.9353 Epoch 489/1000, Train Loss: 0.1421, Train Accuracy: 0.9506, Val Loss: 0.1627, Val Accuracy: 0.9353 Epoch 490/1000, Train Loss: 0.1436, Train Accuracy: 0.9507, Val Loss: 0.1626, Val Accuracy: 0.9353 Epoch 491/1000, Train Loss: 0.1389, Train Accuracy: 0.9529, Val Loss: 0.1626, Val Accuracy: 0.9353 Epoch 492/1000, Train Loss: 0.1391, Train Accuracy: 0.9523, Val Loss: 0.1626, Val Accuracy: 0.9353 Epoch 493/1000, Train Loss: 0.1404, Train Accuracy: 0.9511, Val Loss: 0.1622, Val Accuracy: 0.9353 Epoch 494/1000, Train Loss: 0.1408, Train Accuracy: 0.9511, Val Loss: 0.1620, Val Accuracy: 0.9353 Epoch 495/1000, Train Loss: 0.1358, Train Accuracy: 0.9535, Val Loss: 0.1618, Val Accuracy: 0.9353 Epoch 496/1000, Train Loss: 0.1394, Train Accuracy: 0.9546, Val Loss: 0.1615, Val Accuracy: 0.9353 Epoch 497/1000, Train Loss: 0.1400, Train Accuracy: 0.9541, Val Loss: 0.1612, Val Accuracy: 0.9353 Epoch 498/1000, Train Loss: 0.1401, Train Accuracy: 0.9513, Val Loss: 0.1609, Val Accuracy: 0.9353 Epoch 499/1000, Train Loss: 0.1406, Train Accuracy: 0.9502, Val Loss: 0.1608, Val Accuracy: 0.9353 Epoch 500/1000, Train Loss: 0.1389, Train Accuracy: 0.9518, Val Loss: 0.1605, Val Accuracy: 0.9358 Epoch 501/1000, Train Loss: 0.1377, Train Accuracy: 0.9522, Val Loss: 0.1604, Val Accuracy: 0.9358 Epoch 502/1000, Train Loss: 0.1368, Train Accuracy: 0.9544, Val Loss: 0.1602, Val Accuracy: 0.9363 Epoch 503/1000, Train Loss: 0.1360, Train Accuracy: 0.9543, Val Loss: 0.1598, Val Accuracy: 0.9363 Epoch 504/1000, Train Loss: 0.1376, Train Accuracy: 0.9511, Val Loss: 0.1597, Val Accuracy: 0.9363 Epoch 505/1000, Train Loss: 0.1325, Train Accuracy: 0.9577, Val Loss: 0.1596, Val Accuracy: 0.9368 Epoch 506/1000, Train Loss: 0.1372, Train Accuracy: 0.9521, Val Loss: 0.1592, Val Accuracy: 0.9373 Epoch 507/1000, Train Loss: 0.1371, Train Accuracy: 0.9527, Val Loss: 0.1591, Val Accuracy: 0.9377 Epoch 508/1000, Train Loss: 0.1342, Train Accuracy: 0.9532, Val Loss: 0.1587, Val Accuracy: 0.9377 Epoch 509/1000, Train Loss: 0.1352, Train Accuracy: 0.9544, Val Loss: 0.1585, Val Accuracy: 0.9377 Epoch 510/1000, Train Loss: 0.1336, Train Accuracy: 0.9548, Val Loss: 0.1584, Val Accuracy: 0.9373 Epoch 511/1000, Train Loss: 0.1371, Train Accuracy: 0.9522, Val Loss: 0.1582, Val Accuracy: 0.9377 Epoch 512/1000, Train Loss: 0.1336, Train Accuracy: 0.9543, Val Loss: 0.1580, Val Accuracy: 0.9377 Epoch 513/1000, Train Loss: 0.1334, Train Accuracy: 0.9567, Val Loss: 0.1578, Val Accuracy: 0.9382 Epoch 514/1000, Train Loss: 0.1345, Train Accuracy: 0.9540, Val Loss: 0.1579, Val Accuracy: 0.9377 Epoch 515/1000, Train Loss: 0.1355, Train Accuracy: 0.9533, Val Loss: 0.1574, Val Accuracy: 0.9377 Epoch 516/1000, Train Loss: 0.1297, Train Accuracy: 0.9546, Val Loss: 0.1572, Val Accuracy: 0.9377 Epoch 517/1000, Train Loss: 0.1313, Train Accuracy: 0.9520, Val Loss: 0.1568, Val Accuracy: 0.9377 Epoch 518/1000, Train Loss: 0.1333, Train Accuracy: 0.9530, Val Loss: 0.1568, Val Accuracy: 0.9377 Epoch 519/1000, Train Loss: 0.1283, Train Accuracy: 0.9589, Val Loss: 0.1565, Val Accuracy: 0.9377 Epoch 520/1000, Train Loss: 0.1308, Train Accuracy: 0.9560, Val Loss: 0.1564, Val Accuracy: 0.9382 Epoch 521/1000, Train Loss: 0.1323, Train Accuracy: 0.9560, Val Loss: 0.1559, Val Accuracy: 0.9382 Epoch 522/1000, Train Loss: 0.1328, Train Accuracy: 0.9537, Val Loss: 0.1555, Val Accuracy: 0.9392 Epoch 523/1000, Train Loss: 0.1312, Train Accuracy: 0.9557, Val Loss: 0.1553, Val Accuracy: 0.9392 Epoch 524/1000, Train Loss: 0.1342, Train Accuracy: 0.9518, Val Loss: 0.1552, Val Accuracy: 0.9392 Epoch 525/1000, Train Loss: 0.1309, Train Accuracy: 0.9557, Val Loss: 0.1551, Val Accuracy: 0.9387 Epoch 526/1000, Train Loss: 0.1289, Train Accuracy: 0.9565, Val Loss: 0.1550, Val Accuracy: 0.9387 Epoch 527/1000, Train Loss: 0.1315, Train Accuracy: 0.9545, Val Loss: 0.1549, Val Accuracy: 0.9387 Epoch 528/1000, Train Loss: 0.1295, Train Accuracy: 0.9573, Val Loss: 0.1546, Val Accuracy: 0.9387 Epoch 529/1000, Train Loss: 0.1294, Train Accuracy: 0.9574, Val Loss: 0.1547, Val Accuracy: 0.9382 Epoch 530/1000, Train Loss: 0.1293, Train Accuracy: 0.9555, Val Loss: 0.1544, Val Accuracy: 0.9387 Epoch 531/1000, Train Loss: 0.1294, Train Accuracy: 0.9571, Val Loss: 0.1544, Val Accuracy: 0.9392 Epoch 532/1000, Train Loss: 0.1276, Train Accuracy: 0.9575, Val Loss: 0.1539, Val Accuracy: 0.9397 Epoch 533/1000, Train Loss: 0.1285, Train Accuracy: 0.9562, Val Loss: 0.1537, Val Accuracy: 0.9397 Epoch 534/1000, Train Loss: 0.1274, Train Accuracy: 0.9578, Val Loss: 0.1536, Val Accuracy: 0.9402 Epoch 535/1000, Train Loss: 0.1251, Train Accuracy: 0.9580, Val Loss: 0.1534, Val Accuracy: 0.9407 Epoch 536/1000, Train Loss: 0.1262, Train Accuracy: 0.9578, Val Loss: 0.1533, Val Accuracy: 0.9397 Epoch 537/1000, Train Loss: 0.1282, Train Accuracy: 0.9551, Val Loss: 0.1532, Val Accuracy: 0.9397 Epoch 538/1000, Train Loss: 0.1284, Train Accuracy: 0.9588, Val Loss: 0.1534, Val Accuracy: 0.9397 Epoch 539/1000, Train Loss: 0.1268, Train Accuracy: 0.9556, Val Loss: 0.1531, Val Accuracy: 0.9397 Epoch 540/1000, Train Loss: 0.1255, Train Accuracy: 0.9572, Val Loss: 0.1527, Val Accuracy: 0.9397 Epoch 541/1000, Train Loss: 0.1258, Train Accuracy: 0.9590, Val Loss: 0.1526, Val Accuracy: 0.9402 Epoch 542/1000, Train Loss: 0.1283, Train Accuracy: 0.9563, Val Loss: 0.1523, Val Accuracy: 0.9407 Epoch 543/1000, Train Loss: 0.1228, Train Accuracy: 0.9585, Val Loss: 0.1521, Val Accuracy: 0.9402 Epoch 544/1000, Train Loss: 0.1264, Train Accuracy: 0.9579, Val Loss: 0.1519, Val Accuracy: 0.9407 Epoch 545/1000, Train Loss: 0.1220, Train Accuracy: 0.9586, Val Loss: 0.1517, Val Accuracy: 0.9411 Epoch 546/1000, Train Loss: 0.1266, Train Accuracy: 0.9589, Val Loss: 0.1514, Val Accuracy: 0.9411 Epoch 547/1000, Train Loss: 0.1227, Train Accuracy: 0.9582, Val Loss: 0.1511, Val Accuracy: 0.9411 Epoch 548/1000, Train Loss: 0.1241, Train Accuracy: 0.9555, Val Loss: 0.1509, Val Accuracy: 0.9416 Epoch 549/1000, Train Loss: 0.1226, Train Accuracy: 0.9586, Val Loss: 0.1512, Val Accuracy: 0.9407 Epoch 550/1000, Train Loss: 0.1204, Train Accuracy: 0.9600, Val Loss: 0.1510, Val Accuracy: 0.9407 Epoch 551/1000, Train Loss: 0.1245, Train Accuracy: 0.9574, Val Loss: 0.1509, Val Accuracy: 0.9402 Epoch 552/1000, Train Loss: 0.1232, Train Accuracy: 0.9565, Val Loss: 0.1505, Val Accuracy: 0.9407 Epoch 553/1000, Train Loss: 0.1201, Train Accuracy: 0.9583, Val Loss: 0.1504, Val Accuracy: 0.9407 Epoch 554/1000, Train Loss: 0.1215, Train Accuracy: 0.9579, Val Loss: 0.1504, Val Accuracy: 0.9407 Epoch 555/1000, Train Loss: 0.1208, Train Accuracy: 0.9600, Val Loss: 0.1501, Val Accuracy: 0.9411 Epoch 556/1000, Train Loss: 0.1219, Train Accuracy: 0.9584, Val Loss: 0.1499, Val Accuracy: 0.9411 Epoch 557/1000, Train Loss: 0.1245, Train Accuracy: 0.9580, Val Loss: 0.1498, Val Accuracy: 0.9407 Epoch 558/1000, Train Loss: 0.1231, Train Accuracy: 0.9574, Val Loss: 0.1496, Val Accuracy: 0.9407 Epoch 559/1000, Train Loss: 0.1202, Train Accuracy: 0.9585, Val Loss: 0.1495, Val Accuracy: 0.9402 Epoch 560/1000, Train Loss: 0.1174, Train Accuracy: 0.9610, Val Loss: 0.1491, Val Accuracy: 0.9407 Epoch 561/1000, Train Loss: 0.1197, Train Accuracy: 0.9611, Val Loss: 0.1488, Val Accuracy: 0.9411 Epoch 562/1000, Train Loss: 0.1189, Train Accuracy: 0.9590, Val Loss: 0.1486, Val Accuracy: 0.9411 Epoch 563/1000, Train Loss: 0.1193, Train Accuracy: 0.9593, Val Loss: 0.1484, Val Accuracy: 0.9411 Epoch 564/1000, Train Loss: 0.1163, Train Accuracy: 0.9620, Val Loss: 0.1481, Val Accuracy: 0.9416 Epoch 565/1000, Train Loss: 0.1176, Train Accuracy: 0.9597, Val Loss: 0.1482, Val Accuracy: 0.9416 Epoch 566/1000, Train Loss: 0.1178, Train Accuracy: 0.9619, Val Loss: 0.1478, Val Accuracy: 0.9416 Epoch 567/1000, Train Loss: 0.1154, Train Accuracy: 0.9595, Val Loss: 0.1479, Val Accuracy: 0.9411 Epoch 568/1000, Train Loss: 0.1192, Train Accuracy: 0.9580, Val Loss: 0.1478, Val Accuracy: 0.9411 Epoch 569/1000, Train Loss: 0.1188, Train Accuracy: 0.9611, Val Loss: 0.1478, Val Accuracy: 0.9411 Epoch 570/1000, Train Loss: 0.1161, Train Accuracy: 0.9603, Val Loss: 0.1478, Val Accuracy: 0.9411 Epoch 571/1000, Train Loss: 0.1145, Train Accuracy: 0.9608, Val Loss: 0.1475, Val Accuracy: 0.9411 Epoch 572/1000, Train Loss: 0.1168, Train Accuracy: 0.9594, Val Loss: 0.1476, Val Accuracy: 0.9411 Epoch 573/1000, Train Loss: 0.1175, Train Accuracy: 0.9601, Val Loss: 0.1474, Val Accuracy: 0.9407 Epoch 574/1000, Train Loss: 0.1150, Train Accuracy: 0.9619, Val Loss: 0.1472, Val Accuracy: 0.9407 Epoch 575/1000, Train Loss: 0.1164, Train Accuracy: 0.9596, Val Loss: 0.1468, Val Accuracy: 0.9421 Epoch 576/1000, Train Loss: 0.1172, Train Accuracy: 0.9614, Val Loss: 0.1467, Val Accuracy: 0.9421 Epoch 577/1000, Train Loss: 0.1162, Train Accuracy: 0.9614, Val Loss: 0.1465, Val Accuracy: 0.9421 Epoch 578/1000, Train Loss: 0.1131, Train Accuracy: 0.9629, Val Loss: 0.1463, Val Accuracy: 0.9421 Epoch 579/1000, Train Loss: 0.1171, Train Accuracy: 0.9595, Val Loss: 0.1461, Val Accuracy: 0.9421 Epoch 580/1000, Train Loss: 0.1169, Train Accuracy: 0.9601, Val Loss: 0.1459, Val Accuracy: 0.9416 Epoch 581/1000, Train Loss: 0.1167, Train Accuracy: 0.9600, Val Loss: 0.1457, Val Accuracy: 0.9416 Epoch 582/1000, Train Loss: 0.1142, Train Accuracy: 0.9597, Val Loss: 0.1458, Val Accuracy: 0.9416 Epoch 583/1000, Train Loss: 0.1152, Train Accuracy: 0.9612, Val Loss: 0.1456, Val Accuracy: 0.9421 Epoch 584/1000, Train Loss: 0.1139, Train Accuracy: 0.9611, Val Loss: 0.1456, Val Accuracy: 0.9426 Epoch 585/1000, Train Loss: 0.1105, Train Accuracy: 0.9629, Val Loss: 0.1456, Val Accuracy: 0.9421 Epoch 586/1000, Train Loss: 0.1096, Train Accuracy: 0.9629, Val Loss: 0.1454, Val Accuracy: 0.9426 Epoch 587/1000, Train Loss: 0.1120, Train Accuracy: 0.9634, Val Loss: 0.1453, Val Accuracy: 0.9426 Epoch 588/1000, Train Loss: 0.1141, Train Accuracy: 0.9610, Val Loss: 0.1453, Val Accuracy: 0.9426 Epoch 589/1000, Train Loss: 0.1116, Train Accuracy: 0.9627, Val Loss: 0.1452, Val Accuracy: 0.9426 Epoch 590/1000, Train Loss: 0.1105, Train Accuracy: 0.9640, Val Loss: 0.1452, Val Accuracy: 0.9426 Epoch 591/1000, Train Loss: 0.1118, Train Accuracy: 0.9641, Val Loss: 0.1450, Val Accuracy: 0.9431 Epoch 592/1000, Train Loss: 0.1103, Train Accuracy: 0.9630, Val Loss: 0.1448, Val Accuracy: 0.9431 Epoch 593/1000, Train Loss: 0.1106, Train Accuracy: 0.9633, Val Loss: 0.1446, Val Accuracy: 0.9436 Epoch 594/1000, Train Loss: 0.1087, Train Accuracy: 0.9647, Val Loss: 0.1446, Val Accuracy: 0.9436 Epoch 595/1000, Train Loss: 0.1084, Train Accuracy: 0.9624, Val Loss: 0.1442, Val Accuracy: 0.9436 Epoch 596/1000, Train Loss: 0.1059, Train Accuracy: 0.9655, Val Loss: 0.1439, Val Accuracy: 0.9431 Epoch 597/1000, Train Loss: 0.1086, Train Accuracy: 0.9641, Val Loss: 0.1437, Val Accuracy: 0.9436 Epoch 598/1000, Train Loss: 0.1085, Train Accuracy: 0.9631, Val Loss: 0.1435, Val Accuracy: 0.9436 Epoch 599/1000, Train Loss: 0.1117, Train Accuracy: 0.9638, Val Loss: 0.1433, Val Accuracy: 0.9436 Epoch 600/1000, Train Loss: 0.1085, Train Accuracy: 0.9644, Val Loss: 0.1430, Val Accuracy: 0.9436 Epoch 601/1000, Train Loss: 0.1099, Train Accuracy: 0.9630, Val Loss: 0.1431, Val Accuracy: 0.9436 Epoch 602/1000, Train Loss: 0.1069, Train Accuracy: 0.9647, Val Loss: 0.1430, Val Accuracy: 0.9431 Epoch 603/1000, Train Loss: 0.1072, Train Accuracy: 0.9631, Val Loss: 0.1431, Val Accuracy: 0.9431 Epoch 604/1000, Train Loss: 0.1106, Train Accuracy: 0.9612, Val Loss: 0.1431, Val Accuracy: 0.9431 Epoch 605/1000, Train Loss: 0.1052, Train Accuracy: 0.9648, Val Loss: 0.1426, Val Accuracy: 0.9436 Epoch 606/1000, Train Loss: 0.1064, Train Accuracy: 0.9644, Val Loss: 0.1425, Val Accuracy: 0.9441 Epoch 607/1000, Train Loss: 0.1072, Train Accuracy: 0.9625, Val Loss: 0.1424, Val Accuracy: 0.9436 Epoch 608/1000, Train Loss: 0.1070, Train Accuracy: 0.9638, Val Loss: 0.1423, Val Accuracy: 0.9436 Epoch 609/1000, Train Loss: 0.1064, Train Accuracy: 0.9652, Val Loss: 0.1422, Val Accuracy: 0.9436 Epoch 610/1000, Train Loss: 0.1044, Train Accuracy: 0.9653, Val Loss: 0.1419, Val Accuracy: 0.9436 Epoch 611/1000, Train Loss: 0.1054, Train Accuracy: 0.9656, Val Loss: 0.1416, Val Accuracy: 0.9450 Epoch 612/1000, Train Loss: 0.1059, Train Accuracy: 0.9645, Val Loss: 0.1413, Val Accuracy: 0.9460 Epoch 613/1000, Train Loss: 0.1072, Train Accuracy: 0.9639, Val Loss: 0.1415, Val Accuracy: 0.9441 Epoch 614/1000, Train Loss: 0.1063, Train Accuracy: 0.9642, Val Loss: 0.1415, Val Accuracy: 0.9441 Epoch 615/1000, Train Loss: 0.1045, Train Accuracy: 0.9653, Val Loss: 0.1413, Val Accuracy: 0.9446 Epoch 616/1000, Train Loss: 0.1072, Train Accuracy: 0.9634, Val Loss: 0.1412, Val Accuracy: 0.9450 Epoch 617/1000, Train Loss: 0.1023, Train Accuracy: 0.9664, Val Loss: 0.1412, Val Accuracy: 0.9446 Epoch 618/1000, Train Loss: 0.1044, Train Accuracy: 0.9650, Val Loss: 0.1409, Val Accuracy: 0.9450 Epoch 619/1000, Train Loss: 0.1025, Train Accuracy: 0.9675, Val Loss: 0.1409, Val Accuracy: 0.9446 Epoch 620/1000, Train Loss: 0.1011, Train Accuracy: 0.9676, Val Loss: 0.1407, Val Accuracy: 0.9465 Epoch 621/1000, Train Loss: 0.1035, Train Accuracy: 0.9635, Val Loss: 0.1408, Val Accuracy: 0.9460 Epoch 622/1000, Train Loss: 0.1044, Train Accuracy: 0.9651, Val Loss: 0.1409, Val Accuracy: 0.9460 Epoch 623/1000, Train Loss: 0.1009, Train Accuracy: 0.9667, Val Loss: 0.1406, Val Accuracy: 0.9455 Epoch 624/1000, Train Loss: 0.1025, Train Accuracy: 0.9665, Val Loss: 0.1404, Val Accuracy: 0.9455 Epoch 625/1000, Train Loss: 0.1038, Train Accuracy: 0.9664, Val Loss: 0.1401, Val Accuracy: 0.9470 Epoch 626/1000, Train Loss: 0.1008, Train Accuracy: 0.9668, Val Loss: 0.1400, Val Accuracy: 0.9465 Epoch 627/1000, Train Loss: 0.1039, Train Accuracy: 0.9656, Val Loss: 0.1402, Val Accuracy: 0.9460 Epoch 628/1000, Train Loss: 0.1011, Train Accuracy: 0.9672, Val Loss: 0.1402, Val Accuracy: 0.9460 Epoch 629/1000, Train Loss: 0.0978, Train Accuracy: 0.9690, Val Loss: 0.1399, Val Accuracy: 0.9465 Epoch 630/1000, Train Loss: 0.1010, Train Accuracy: 0.9650, Val Loss: 0.1398, Val Accuracy: 0.9465 Epoch 631/1000, Train Loss: 0.1026, Train Accuracy: 0.9665, Val Loss: 0.1396, Val Accuracy: 0.9470 Epoch 632/1000, Train Loss: 0.1026, Train Accuracy: 0.9676, Val Loss: 0.1396, Val Accuracy: 0.9460 Epoch 633/1000, Train Loss: 0.1003, Train Accuracy: 0.9656, Val Loss: 0.1393, Val Accuracy: 0.9470 Epoch 634/1000, Train Loss: 0.0996, Train Accuracy: 0.9680, Val Loss: 0.1393, Val Accuracy: 0.9470 Epoch 635/1000, Train Loss: 0.1026, Train Accuracy: 0.9655, Val Loss: 0.1393, Val Accuracy: 0.9465 Epoch 636/1000, Train Loss: 0.1020, Train Accuracy: 0.9659, Val Loss: 0.1393, Val Accuracy: 0.9465 Epoch 637/1000, Train Loss: 0.1025, Train Accuracy: 0.9661, Val Loss: 0.1391, Val Accuracy: 0.9470 Epoch 638/1000, Train Loss: 0.0985, Train Accuracy: 0.9662, Val Loss: 0.1389, Val Accuracy: 0.9470 Epoch 639/1000, Train Loss: 0.0989, Train Accuracy: 0.9670, Val Loss: 0.1389, Val Accuracy: 0.9470 Epoch 640/1000, Train Loss: 0.1001, Train Accuracy: 0.9653, Val Loss: 0.1387, Val Accuracy: 0.9470 Epoch 641/1000, Train Loss: 0.0973, Train Accuracy: 0.9681, Val Loss: 0.1385, Val Accuracy: 0.9470 Epoch 642/1000, Train Loss: 0.0967, Train Accuracy: 0.9683, Val Loss: 0.1385, Val Accuracy: 0.9470 Epoch 643/1000, Train Loss: 0.0977, Train Accuracy: 0.9683, Val Loss: 0.1385, Val Accuracy: 0.9470 Epoch 644/1000, Train Loss: 0.0981, Train Accuracy: 0.9664, Val Loss: 0.1381, Val Accuracy: 0.9470 Epoch 645/1000, Train Loss: 0.0949, Train Accuracy: 0.9691, Val Loss: 0.1380, Val Accuracy: 0.9470 Epoch 646/1000, Train Loss: 0.0977, Train Accuracy: 0.9690, Val Loss: 0.1381, Val Accuracy: 0.9470 Epoch 647/1000, Train Loss: 0.0954, Train Accuracy: 0.9673, Val Loss: 0.1379, Val Accuracy: 0.9470 Epoch 648/1000, Train Loss: 0.0961, Train Accuracy: 0.9684, Val Loss: 0.1381, Val Accuracy: 0.9470 Epoch 649/1000, Train Loss: 0.0971, Train Accuracy: 0.9675, Val Loss: 0.1381, Val Accuracy: 0.9470 Epoch 650/1000, Train Loss: 0.0936, Train Accuracy: 0.9707, Val Loss: 0.1379, Val Accuracy: 0.9470 Epoch 651/1000, Train Loss: 0.0975, Train Accuracy: 0.9678, Val Loss: 0.1375, Val Accuracy: 0.9470 Epoch 652/1000, Train Loss: 0.0961, Train Accuracy: 0.9669, Val Loss: 0.1376, Val Accuracy: 0.9470 Epoch 653/1000, Train Loss: 0.0978, Train Accuracy: 0.9678, Val Loss: 0.1374, Val Accuracy: 0.9470 Epoch 654/1000, Train Loss: 0.0946, Train Accuracy: 0.9706, Val Loss: 0.1372, Val Accuracy: 0.9475 Epoch 655/1000, Train Loss: 0.0966, Train Accuracy: 0.9673, Val Loss: 0.1371, Val Accuracy: 0.9470 Epoch 656/1000, Train Loss: 0.0941, Train Accuracy: 0.9700, Val Loss: 0.1372, Val Accuracy: 0.9470 Epoch 657/1000, Train Loss: 0.0962, Train Accuracy: 0.9680, Val Loss: 0.1373, Val Accuracy: 0.9470 Epoch 658/1000, Train Loss: 0.0924, Train Accuracy: 0.9697, Val Loss: 0.1370, Val Accuracy: 0.9475 Epoch 659/1000, Train Loss: 0.0959, Train Accuracy: 0.9683, Val Loss: 0.1369, Val Accuracy: 0.9475 Epoch 660/1000, Train Loss: 0.0935, Train Accuracy: 0.9693, Val Loss: 0.1368, Val Accuracy: 0.9480 Epoch 661/1000, Train Loss: 0.0942, Train Accuracy: 0.9700, Val Loss: 0.1367, Val Accuracy: 0.9480 Epoch 662/1000, Train Loss: 0.0954, Train Accuracy: 0.9685, Val Loss: 0.1366, Val Accuracy: 0.9480 Epoch 663/1000, Train Loss: 0.0949, Train Accuracy: 0.9673, Val Loss: 0.1364, Val Accuracy: 0.9480 Epoch 664/1000, Train Loss: 0.0917, Train Accuracy: 0.9706, Val Loss: 0.1365, Val Accuracy: 0.9480 Epoch 665/1000, Train Loss: 0.0940, Train Accuracy: 0.9687, Val Loss: 0.1366, Val Accuracy: 0.9480 Epoch 666/1000, Train Loss: 0.0948, Train Accuracy: 0.9673, Val Loss: 0.1365, Val Accuracy: 0.9480 Epoch 667/1000, Train Loss: 0.0935, Train Accuracy: 0.9679, Val Loss: 0.1364, Val Accuracy: 0.9480 Epoch 668/1000, Train Loss: 0.0938, Train Accuracy: 0.9693, Val Loss: 0.1361, Val Accuracy: 0.9480 Epoch 669/1000, Train Loss: 0.0906, Train Accuracy: 0.9685, Val Loss: 0.1362, Val Accuracy: 0.9480 Epoch 670/1000, Train Loss: 0.0906, Train Accuracy: 0.9708, Val Loss: 0.1361, Val Accuracy: 0.9480 Epoch 671/1000, Train Loss: 0.0914, Train Accuracy: 0.9692, Val Loss: 0.1361, Val Accuracy: 0.9480 Epoch 672/1000, Train Loss: 0.0927, Train Accuracy: 0.9698, Val Loss: 0.1360, Val Accuracy: 0.9480 Epoch 673/1000, Train Loss: 0.0886, Train Accuracy: 0.9732, Val Loss: 0.1357, Val Accuracy: 0.9480 Epoch 674/1000, Train Loss: 0.0918, Train Accuracy: 0.9704, Val Loss: 0.1355, Val Accuracy: 0.9480 Epoch 675/1000, Train Loss: 0.0909, Train Accuracy: 0.9704, Val Loss: 0.1355, Val Accuracy: 0.9480 Epoch 676/1000, Train Loss: 0.0931, Train Accuracy: 0.9685, Val Loss: 0.1354, Val Accuracy: 0.9480 Epoch 677/1000, Train Loss: 0.0911, Train Accuracy: 0.9697, Val Loss: 0.1354, Val Accuracy: 0.9480 Epoch 678/1000, Train Loss: 0.0902, Train Accuracy: 0.9712, Val Loss: 0.1355, Val Accuracy: 0.9480 Epoch 679/1000, Train Loss: 0.0881, Train Accuracy: 0.9718, Val Loss: 0.1354, Val Accuracy: 0.9480 Epoch 680/1000, Train Loss: 0.0906, Train Accuracy: 0.9698, Val Loss: 0.1352, Val Accuracy: 0.9480 Epoch 681/1000, Train Loss: 0.0906, Train Accuracy: 0.9695, Val Loss: 0.1349, Val Accuracy: 0.9484 Epoch 682/1000, Train Loss: 0.0887, Train Accuracy: 0.9725, Val Loss: 0.1349, Val Accuracy: 0.9480 Epoch 683/1000, Train Loss: 0.0904, Train Accuracy: 0.9704, Val Loss: 0.1350, Val Accuracy: 0.9480 Epoch 684/1000, Train Loss: 0.0891, Train Accuracy: 0.9721, Val Loss: 0.1348, Val Accuracy: 0.9480 Epoch 685/1000, Train Loss: 0.0881, Train Accuracy: 0.9702, Val Loss: 0.1345, Val Accuracy: 0.9484 Epoch 686/1000, Train Loss: 0.0848, Train Accuracy: 0.9743, Val Loss: 0.1344, Val Accuracy: 0.9484 Epoch 687/1000, Train Loss: 0.0882, Train Accuracy: 0.9725, Val Loss: 0.1345, Val Accuracy: 0.9484 Epoch 688/1000, Train Loss: 0.0885, Train Accuracy: 0.9703, Val Loss: 0.1345, Val Accuracy: 0.9480 Epoch 689/1000, Train Loss: 0.0883, Train Accuracy: 0.9710, Val Loss: 0.1347, Val Accuracy: 0.9480 Epoch 690/1000, Train Loss: 0.0878, Train Accuracy: 0.9718, Val Loss: 0.1346, Val Accuracy: 0.9480 Epoch 691/1000, Train Loss: 0.0871, Train Accuracy: 0.9729, Val Loss: 0.1345, Val Accuracy: 0.9484 Epoch 692/1000, Train Loss: 0.0884, Train Accuracy: 0.9718, Val Loss: 0.1342, Val Accuracy: 0.9484 Epoch 693/1000, Train Loss: 0.0871, Train Accuracy: 0.9729, Val Loss: 0.1341, Val Accuracy: 0.9484 Epoch 694/1000, Train Loss: 0.0855, Train Accuracy: 0.9720, Val Loss: 0.1342, Val Accuracy: 0.9484 Epoch 695/1000, Train Loss: 0.0837, Train Accuracy: 0.9756, Val Loss: 0.1338, Val Accuracy: 0.9484 Epoch 696/1000, Train Loss: 0.0874, Train Accuracy: 0.9710, Val Loss: 0.1340, Val Accuracy: 0.9484 Epoch 697/1000, Train Loss: 0.0855, Train Accuracy: 0.9724, Val Loss: 0.1339, Val Accuracy: 0.9484 Epoch 698/1000, Train Loss: 0.0838, Train Accuracy: 0.9745, Val Loss: 0.1337, Val Accuracy: 0.9484 Epoch 699/1000, Train Loss: 0.0851, Train Accuracy: 0.9724, Val Loss: 0.1338, Val Accuracy: 0.9484 Epoch 700/1000, Train Loss: 0.0879, Train Accuracy: 0.9719, Val Loss: 0.1337, Val Accuracy: 0.9484 Epoch 701/1000, Train Loss: 0.0853, Train Accuracy: 0.9732, Val Loss: 0.1336, Val Accuracy: 0.9484 Epoch 702/1000, Train Loss: 0.0841, Train Accuracy: 0.9741, Val Loss: 0.1335, Val Accuracy: 0.9484 Epoch 703/1000, Train Loss: 0.0871, Train Accuracy: 0.9704, Val Loss: 0.1335, Val Accuracy: 0.9484 Epoch 704/1000, Train Loss: 0.0843, Train Accuracy: 0.9718, Val Loss: 0.1334, Val Accuracy: 0.9484 Epoch 705/1000, Train Loss: 0.0855, Train Accuracy: 0.9731, Val Loss: 0.1331, Val Accuracy: 0.9484 Epoch 706/1000, Train Loss: 0.0846, Train Accuracy: 0.9731, Val Loss: 0.1330, Val Accuracy: 0.9484 Epoch 707/1000, Train Loss: 0.0836, Train Accuracy: 0.9738, Val Loss: 0.1330, Val Accuracy: 0.9484 Epoch 708/1000, Train Loss: 0.0838, Train Accuracy: 0.9728, Val Loss: 0.1330, Val Accuracy: 0.9484 Epoch 709/1000, Train Loss: 0.0839, Train Accuracy: 0.9730, Val Loss: 0.1330, Val Accuracy: 0.9484 Epoch 710/1000, Train Loss: 0.0830, Train Accuracy: 0.9746, Val Loss: 0.1329, Val Accuracy: 0.9489 Epoch 711/1000, Train Loss: 0.0795, Train Accuracy: 0.9751, Val Loss: 0.1329, Val Accuracy: 0.9484 Epoch 712/1000, Train Loss: 0.0824, Train Accuracy: 0.9738, Val Loss: 0.1328, Val Accuracy: 0.9484 Epoch 713/1000, Train Loss: 0.0823, Train Accuracy: 0.9725, Val Loss: 0.1329, Val Accuracy: 0.9475 Epoch 714/1000, Train Loss: 0.0840, Train Accuracy: 0.9730, Val Loss: 0.1327, Val Accuracy: 0.9484 Epoch 715/1000, Train Loss: 0.0829, Train Accuracy: 0.9737, Val Loss: 0.1327, Val Accuracy: 0.9484 Epoch 716/1000, Train Loss: 0.0827, Train Accuracy: 0.9724, Val Loss: 0.1325, Val Accuracy: 0.9480 Epoch 717/1000, Train Loss: 0.0848, Train Accuracy: 0.9718, Val Loss: 0.1325, Val Accuracy: 0.9480 Epoch 718/1000, Train Loss: 0.0807, Train Accuracy: 0.9747, Val Loss: 0.1326, Val Accuracy: 0.9480 Epoch 719/1000, Train Loss: 0.0835, Train Accuracy: 0.9730, Val Loss: 0.1325, Val Accuracy: 0.9484 Epoch 720/1000, Train Loss: 0.0798, Train Accuracy: 0.9747, Val Loss: 0.1325, Val Accuracy: 0.9484 Epoch 721/1000, Train Loss: 0.0815, Train Accuracy: 0.9743, Val Loss: 0.1325, Val Accuracy: 0.9484 Epoch 722/1000, Train Loss: 0.0821, Train Accuracy: 0.9745, Val Loss: 0.1324, Val Accuracy: 0.9484 Epoch 723/1000, Train Loss: 0.0793, Train Accuracy: 0.9746, Val Loss: 0.1323, Val Accuracy: 0.9484 Epoch 724/1000, Train Loss: 0.0817, Train Accuracy: 0.9741, Val Loss: 0.1324, Val Accuracy: 0.9484 Epoch 725/1000, Train Loss: 0.0832, Train Accuracy: 0.9726, Val Loss: 0.1323, Val Accuracy: 0.9484 Epoch 726/1000, Train Loss: 0.0811, Train Accuracy: 0.9736, Val Loss: 0.1321, Val Accuracy: 0.9480 Epoch 727/1000, Train Loss: 0.0814, Train Accuracy: 0.9721, Val Loss: 0.1320, Val Accuracy: 0.9484 Epoch 728/1000, Train Loss: 0.0792, Train Accuracy: 0.9746, Val Loss: 0.1317, Val Accuracy: 0.9480 Epoch 729/1000, Train Loss: 0.0819, Train Accuracy: 0.9738, Val Loss: 0.1315, Val Accuracy: 0.9480 Epoch 730/1000, Train Loss: 0.0849, Train Accuracy: 0.9725, Val Loss: 0.1318, Val Accuracy: 0.9484 Epoch 731/1000, Train Loss: 0.0795, Train Accuracy: 0.9745, Val Loss: 0.1316, Val Accuracy: 0.9480 Epoch 732/1000, Train Loss: 0.0795, Train Accuracy: 0.9736, Val Loss: 0.1313, Val Accuracy: 0.9480 Epoch 733/1000, Train Loss: 0.0788, Train Accuracy: 0.9754, Val Loss: 0.1314, Val Accuracy: 0.9480 Epoch 734/1000, Train Loss: 0.0751, Train Accuracy: 0.9775, Val Loss: 0.1314, Val Accuracy: 0.9480 Epoch 735/1000, Train Loss: 0.0794, Train Accuracy: 0.9749, Val Loss: 0.1313, Val Accuracy: 0.9480 Epoch 736/1000, Train Loss: 0.0813, Train Accuracy: 0.9740, Val Loss: 0.1311, Val Accuracy: 0.9494 Epoch 737/1000, Train Loss: 0.0788, Train Accuracy: 0.9746, Val Loss: 0.1313, Val Accuracy: 0.9480 Epoch 738/1000, Train Loss: 0.0835, Train Accuracy: 0.9741, Val Loss: 0.1313, Val Accuracy: 0.9480 Epoch 739/1000, Train Loss: 0.0805, Train Accuracy: 0.9751, Val Loss: 0.1311, Val Accuracy: 0.9484 Epoch 740/1000, Train Loss: 0.0769, Train Accuracy: 0.9763, Val Loss: 0.1310, Val Accuracy: 0.9484 Epoch 741/1000, Train Loss: 0.0768, Train Accuracy: 0.9760, Val Loss: 0.1309, Val Accuracy: 0.9484 Epoch 742/1000, Train Loss: 0.0779, Train Accuracy: 0.9743, Val Loss: 0.1308, Val Accuracy: 0.9484 Epoch 743/1000, Train Loss: 0.0759, Train Accuracy: 0.9773, Val Loss: 0.1309, Val Accuracy: 0.9475 Epoch 744/1000, Train Loss: 0.0765, Train Accuracy: 0.9771, Val Loss: 0.1308, Val Accuracy: 0.9475 Epoch 745/1000, Train Loss: 0.0780, Train Accuracy: 0.9746, Val Loss: 0.1306, Val Accuracy: 0.9480 Epoch 746/1000, Train Loss: 0.0791, Train Accuracy: 0.9751, Val Loss: 0.1307, Val Accuracy: 0.9475 Epoch 747/1000, Train Loss: 0.0755, Train Accuracy: 0.9769, Val Loss: 0.1308, Val Accuracy: 0.9475 Epoch 748/1000, Train Loss: 0.0754, Train Accuracy: 0.9769, Val Loss: 0.1307, Val Accuracy: 0.9475 Epoch 749/1000, Train Loss: 0.0746, Train Accuracy: 0.9758, Val Loss: 0.1310, Val Accuracy: 0.9484 Epoch 750/1000, Train Loss: 0.0724, Train Accuracy: 0.9782, Val Loss: 0.1309, Val Accuracy: 0.9489 Epoch 751/1000, Train Loss: 0.0801, Train Accuracy: 0.9748, Val Loss: 0.1306, Val Accuracy: 0.9484 Epoch 752/1000, Train Loss: 0.0775, Train Accuracy: 0.9751, Val Loss: 0.1304, Val Accuracy: 0.9489 Epoch 753/1000, Train Loss: 0.0736, Train Accuracy: 0.9777, Val Loss: 0.1304, Val Accuracy: 0.9484 Epoch 754/1000, Train Loss: 0.0734, Train Accuracy: 0.9786, Val Loss: 0.1304, Val Accuracy: 0.9484 Epoch 755/1000, Train Loss: 0.0744, Train Accuracy: 0.9769, Val Loss: 0.1302, Val Accuracy: 0.9484 Epoch 756/1000, Train Loss: 0.0751, Train Accuracy: 0.9765, Val Loss: 0.1303, Val Accuracy: 0.9484 Epoch 757/1000, Train Loss: 0.0755, Train Accuracy: 0.9762, Val Loss: 0.1303, Val Accuracy: 0.9480 Epoch 758/1000, Train Loss: 0.0743, Train Accuracy: 0.9775, Val Loss: 0.1303, Val Accuracy: 0.9484 Epoch 759/1000, Train Loss: 0.0751, Train Accuracy: 0.9757, Val Loss: 0.1300, Val Accuracy: 0.9484 Epoch 760/1000, Train Loss: 0.0735, Train Accuracy: 0.9760, Val Loss: 0.1298, Val Accuracy: 0.9484 Epoch 761/1000, Train Loss: 0.0746, Train Accuracy: 0.9770, Val Loss: 0.1297, Val Accuracy: 0.9475 Epoch 762/1000, Train Loss: 0.0726, Train Accuracy: 0.9777, Val Loss: 0.1297, Val Accuracy: 0.9480 Epoch 763/1000, Train Loss: 0.0734, Train Accuracy: 0.9762, Val Loss: 0.1299, Val Accuracy: 0.9480 Epoch 764/1000, Train Loss: 0.0730, Train Accuracy: 0.9781, Val Loss: 0.1299, Val Accuracy: 0.9480 Epoch 765/1000, Train Loss: 0.0723, Train Accuracy: 0.9775, Val Loss: 0.1299, Val Accuracy: 0.9475 Epoch 766/1000, Train Loss: 0.0703, Train Accuracy: 0.9790, Val Loss: 0.1298, Val Accuracy: 0.9475 Epoch 767/1000, Train Loss: 0.0729, Train Accuracy: 0.9779, Val Loss: 0.1298, Val Accuracy: 0.9480 Epoch 768/1000, Train Loss: 0.0739, Train Accuracy: 0.9766, Val Loss: 0.1295, Val Accuracy: 0.9475 Epoch 769/1000, Train Loss: 0.0710, Train Accuracy: 0.9771, Val Loss: 0.1296, Val Accuracy: 0.9475 Epoch 770/1000, Train Loss: 0.0715, Train Accuracy: 0.9771, Val Loss: 0.1293, Val Accuracy: 0.9489 Epoch 771/1000, Train Loss: 0.0719, Train Accuracy: 0.9766, Val Loss: 0.1296, Val Accuracy: 0.9489 Epoch 772/1000, Train Loss: 0.0722, Train Accuracy: 0.9768, Val Loss: 0.1297, Val Accuracy: 0.9489 Epoch 773/1000, Train Loss: 0.0683, Train Accuracy: 0.9785, Val Loss: 0.1296, Val Accuracy: 0.9489 Epoch 774/1000, Train Loss: 0.0696, Train Accuracy: 0.9803, Val Loss: 0.1295, Val Accuracy: 0.9494 Epoch 775/1000, Train Loss: 0.0741, Train Accuracy: 0.9779, Val Loss: 0.1294, Val Accuracy: 0.9484 Epoch 776/1000, Train Loss: 0.0711, Train Accuracy: 0.9777, Val Loss: 0.1294, Val Accuracy: 0.9484 Epoch 777/1000, Train Loss: 0.0713, Train Accuracy: 0.9790, Val Loss: 0.1295, Val Accuracy: 0.9484 Epoch 778/1000, Train Loss: 0.0702, Train Accuracy: 0.9774, Val Loss: 0.1292, Val Accuracy: 0.9484 Epoch 779/1000, Train Loss: 0.0703, Train Accuracy: 0.9786, Val Loss: 0.1291, Val Accuracy: 0.9484 Epoch 780/1000, Train Loss: 0.0668, Train Accuracy: 0.9803, Val Loss: 0.1290, Val Accuracy: 0.9480 Epoch 781/1000, Train Loss: 0.0696, Train Accuracy: 0.9782, Val Loss: 0.1289, Val Accuracy: 0.9480 Epoch 782/1000, Train Loss: 0.0729, Train Accuracy: 0.9752, Val Loss: 0.1287, Val Accuracy: 0.9480 Epoch 783/1000, Train Loss: 0.0679, Train Accuracy: 0.9783, Val Loss: 0.1286, Val Accuracy: 0.9480 Epoch 784/1000, Train Loss: 0.0706, Train Accuracy: 0.9791, Val Loss: 0.1287, Val Accuracy: 0.9480 Epoch 785/1000, Train Loss: 0.0673, Train Accuracy: 0.9803, Val Loss: 0.1286, Val Accuracy: 0.9475 Epoch 786/1000, Train Loss: 0.0708, Train Accuracy: 0.9773, Val Loss: 0.1287, Val Accuracy: 0.9480 Epoch 787/1000, Train Loss: 0.0707, Train Accuracy: 0.9785, Val Loss: 0.1287, Val Accuracy: 0.9480 Epoch 788/1000, Train Loss: 0.0680, Train Accuracy: 0.9786, Val Loss: 0.1285, Val Accuracy: 0.9480 Epoch 789/1000, Train Loss: 0.0692, Train Accuracy: 0.9765, Val Loss: 0.1285, Val Accuracy: 0.9480 Epoch 790/1000, Train Loss: 0.0683, Train Accuracy: 0.9796, Val Loss: 0.1286, Val Accuracy: 0.9480 Epoch 791/1000, Train Loss: 0.0678, Train Accuracy: 0.9785, Val Loss: 0.1286, Val Accuracy: 0.9480 Epoch 792/1000, Train Loss: 0.0696, Train Accuracy: 0.9776, Val Loss: 0.1283, Val Accuracy: 0.9480 Epoch 793/1000, Train Loss: 0.0640, Train Accuracy: 0.9826, Val Loss: 0.1282, Val Accuracy: 0.9475 Epoch 794/1000, Train Loss: 0.0683, Train Accuracy: 0.9794, Val Loss: 0.1285, Val Accuracy: 0.9489 Epoch 795/1000, Train Loss: 0.0678, Train Accuracy: 0.9788, Val Loss: 0.1283, Val Accuracy: 0.9484 Epoch 796/1000, Train Loss: 0.0686, Train Accuracy: 0.9780, Val Loss: 0.1282, Val Accuracy: 0.9480 Epoch 797/1000, Train Loss: 0.0693, Train Accuracy: 0.9776, Val Loss: 0.1283, Val Accuracy: 0.9484 Epoch 798/1000, Train Loss: 0.0670, Train Accuracy: 0.9796, Val Loss: 0.1284, Val Accuracy: 0.9480 Epoch 799/1000, Train Loss: 0.0658, Train Accuracy: 0.9794, Val Loss: 0.1285, Val Accuracy: 0.9484 Epoch 800/1000, Train Loss: 0.0651, Train Accuracy: 0.9797, Val Loss: 0.1282, Val Accuracy: 0.9484 Epoch 801/1000, Train Loss: 0.0664, Train Accuracy: 0.9805, Val Loss: 0.1284, Val Accuracy: 0.9480 Epoch 802/1000, Train Loss: 0.0659, Train Accuracy: 0.9792, Val Loss: 0.1283, Val Accuracy: 0.9484 Epoch 803/1000, Train Loss: 0.0678, Train Accuracy: 0.9793, Val Loss: 0.1283, Val Accuracy: 0.9489 Epoch 804/1000, Train Loss: 0.0685, Train Accuracy: 0.9794, Val Loss: 0.1279, Val Accuracy: 0.9489 Epoch 805/1000, Train Loss: 0.0658, Train Accuracy: 0.9790, Val Loss: 0.1280, Val Accuracy: 0.9489 Epoch 806/1000, Train Loss: 0.0643, Train Accuracy: 0.9805, Val Loss: 0.1281, Val Accuracy: 0.9489 Epoch 807/1000, Train Loss: 0.0644, Train Accuracy: 0.9796, Val Loss: 0.1280, Val Accuracy: 0.9489 Epoch 808/1000, Train Loss: 0.0641, Train Accuracy: 0.9818, Val Loss: 0.1279, Val Accuracy: 0.9489 Epoch 809/1000, Train Loss: 0.0679, Train Accuracy: 0.9791, Val Loss: 0.1280, Val Accuracy: 0.9484 Epoch 810/1000, Train Loss: 0.0658, Train Accuracy: 0.9819, Val Loss: 0.1280, Val Accuracy: 0.9489 Epoch 811/1000, Train Loss: 0.0647, Train Accuracy: 0.9816, Val Loss: 0.1279, Val Accuracy: 0.9489 Epoch 812/1000, Train Loss: 0.0639, Train Accuracy: 0.9810, Val Loss: 0.1277, Val Accuracy: 0.9484 Epoch 813/1000, Train Loss: 0.0617, Train Accuracy: 0.9827, Val Loss: 0.1277, Val Accuracy: 0.9484 Epoch 814/1000, Train Loss: 0.0680, Train Accuracy: 0.9753, Val Loss: 0.1276, Val Accuracy: 0.9484 Epoch 815/1000, Train Loss: 0.0679, Train Accuracy: 0.9774, Val Loss: 0.1276, Val Accuracy: 0.9489 Epoch 816/1000, Train Loss: 0.0635, Train Accuracy: 0.9803, Val Loss: 0.1275, Val Accuracy: 0.9489 Epoch 817/1000, Train Loss: 0.0649, Train Accuracy: 0.9792, Val Loss: 0.1276, Val Accuracy: 0.9494 Epoch 818/1000, Train Loss: 0.0631, Train Accuracy: 0.9801, Val Loss: 0.1275, Val Accuracy: 0.9489 Epoch 819/1000, Train Loss: 0.0618, Train Accuracy: 0.9816, Val Loss: 0.1277, Val Accuracy: 0.9489 Epoch 820/1000, Train Loss: 0.0630, Train Accuracy: 0.9819, Val Loss: 0.1276, Val Accuracy: 0.9484 Epoch 821/1000, Train Loss: 0.0644, Train Accuracy: 0.9802, Val Loss: 0.1274, Val Accuracy: 0.9475 Epoch 822/1000, Train Loss: 0.0629, Train Accuracy: 0.9808, Val Loss: 0.1276, Val Accuracy: 0.9470 Epoch 823/1000, Train Loss: 0.0600, Train Accuracy: 0.9820, Val Loss: 0.1277, Val Accuracy: 0.9470 Epoch 824/1000, Train Loss: 0.0672, Train Accuracy: 0.9787, Val Loss: 0.1275, Val Accuracy: 0.9470 Epoch 825/1000, Train Loss: 0.0627, Train Accuracy: 0.9828, Val Loss: 0.1273, Val Accuracy: 0.9470 Epoch 826/1000, Train Loss: 0.0602, Train Accuracy: 0.9808, Val Loss: 0.1273, Val Accuracy: 0.9470 Epoch 827/1000, Train Loss: 0.0615, Train Accuracy: 0.9814, Val Loss: 0.1275, Val Accuracy: 0.9470 Epoch 828/1000, Train Loss: 0.0640, Train Accuracy: 0.9810, Val Loss: 0.1274, Val Accuracy: 0.9475 Epoch 829/1000, Train Loss: 0.0621, Train Accuracy: 0.9805, Val Loss: 0.1273, Val Accuracy: 0.9475 Epoch 830/1000, Train Loss: 0.0632, Train Accuracy: 0.9811, Val Loss: 0.1274, Val Accuracy: 0.9475 Epoch 831/1000, Train Loss: 0.0627, Train Accuracy: 0.9813, Val Loss: 0.1276, Val Accuracy: 0.9480 Epoch 832/1000, Train Loss: 0.0654, Train Accuracy: 0.9783, Val Loss: 0.1272, Val Accuracy: 0.9475 Epoch 833/1000, Train Loss: 0.0649, Train Accuracy: 0.9799, Val Loss: 0.1272, Val Accuracy: 0.9465 Epoch 834/1000, Train Loss: 0.0632, Train Accuracy: 0.9792, Val Loss: 0.1270, Val Accuracy: 0.9475 Epoch 835/1000, Train Loss: 0.0609, Train Accuracy: 0.9810, Val Loss: 0.1269, Val Accuracy: 0.9475 Epoch 836/1000, Train Loss: 0.0606, Train Accuracy: 0.9813, Val Loss: 0.1270, Val Accuracy: 0.9475 Epoch 837/1000, Train Loss: 0.0625, Train Accuracy: 0.9807, Val Loss: 0.1269, Val Accuracy: 0.9480 Epoch 838/1000, Train Loss: 0.0594, Train Accuracy: 0.9824, Val Loss: 0.1268, Val Accuracy: 0.9480 Epoch 839/1000, Train Loss: 0.0605, Train Accuracy: 0.9819, Val Loss: 0.1266, Val Accuracy: 0.9480 Epoch 840/1000, Train Loss: 0.0620, Train Accuracy: 0.9818, Val Loss: 0.1265, Val Accuracy: 0.9475 Epoch 841/1000, Train Loss: 0.0607, Train Accuracy: 0.9813, Val Loss: 0.1266, Val Accuracy: 0.9475 Epoch 842/1000, Train Loss: 0.0620, Train Accuracy: 0.9814, Val Loss: 0.1267, Val Accuracy: 0.9480 Epoch 843/1000, Train Loss: 0.0609, Train Accuracy: 0.9822, Val Loss: 0.1266, Val Accuracy: 0.9484 Epoch 844/1000, Train Loss: 0.0597, Train Accuracy: 0.9824, Val Loss: 0.1265, Val Accuracy: 0.9480 Epoch 845/1000, Train Loss: 0.0590, Train Accuracy: 0.9826, Val Loss: 0.1264, Val Accuracy: 0.9484 Epoch 846/1000, Train Loss: 0.0575, Train Accuracy: 0.9838, Val Loss: 0.1265, Val Accuracy: 0.9484 Epoch 847/1000, Train Loss: 0.0583, Train Accuracy: 0.9822, Val Loss: 0.1263, Val Accuracy: 0.9494 Epoch 848/1000, Train Loss: 0.0603, Train Accuracy: 0.9809, Val Loss: 0.1265, Val Accuracy: 0.9484 Epoch 849/1000, Train Loss: 0.0612, Train Accuracy: 0.9808, Val Loss: 0.1267, Val Accuracy: 0.9484 Epoch 850/1000, Train Loss: 0.0585, Train Accuracy: 0.9807, Val Loss: 0.1265, Val Accuracy: 0.9489 Epoch 851/1000, Train Loss: 0.0581, Train Accuracy: 0.9835, Val Loss: 0.1264, Val Accuracy: 0.9489 Epoch 852/1000, Train Loss: 0.0595, Train Accuracy: 0.9808, Val Loss: 0.1266, Val Accuracy: 0.9489 Epoch 853/1000, Train Loss: 0.0578, Train Accuracy: 0.9837, Val Loss: 0.1265, Val Accuracy: 0.9489 Epoch 854/1000, Train Loss: 0.0571, Train Accuracy: 0.9822, Val Loss: 0.1263, Val Accuracy: 0.9494 Epoch 855/1000, Train Loss: 0.0586, Train Accuracy: 0.9832, Val Loss: 0.1262, Val Accuracy: 0.9499 Epoch 856/1000, Train Loss: 0.0578, Train Accuracy: 0.9826, Val Loss: 0.1264, Val Accuracy: 0.9489 Epoch 857/1000, Train Loss: 0.0570, Train Accuracy: 0.9833, Val Loss: 0.1263, Val Accuracy: 0.9494 Epoch 858/1000, Train Loss: 0.0582, Train Accuracy: 0.9828, Val Loss: 0.1264, Val Accuracy: 0.9494 Epoch 859/1000, Train Loss: 0.0582, Train Accuracy: 0.9830, Val Loss: 0.1263, Val Accuracy: 0.9499 Epoch 860/1000, Train Loss: 0.0571, Train Accuracy: 0.9831, Val Loss: 0.1262, Val Accuracy: 0.9499 Epoch 861/1000, Train Loss: 0.0568, Train Accuracy: 0.9846, Val Loss: 0.1261, Val Accuracy: 0.9499 Epoch 862/1000, Train Loss: 0.0569, Train Accuracy: 0.9838, Val Loss: 0.1261, Val Accuracy: 0.9499 Epoch 863/1000, Train Loss: 0.0564, Train Accuracy: 0.9832, Val Loss: 0.1261, Val Accuracy: 0.9499 Epoch 864/1000, Train Loss: 0.0576, Train Accuracy: 0.9837, Val Loss: 0.1265, Val Accuracy: 0.9489 Epoch 865/1000, Train Loss: 0.0567, Train Accuracy: 0.9839, Val Loss: 0.1267, Val Accuracy: 0.9484 Epoch 866/1000, Train Loss: 0.0545, Train Accuracy: 0.9838, Val Loss: 0.1264, Val Accuracy: 0.9489 Epoch 867/1000, Train Loss: 0.0580, Train Accuracy: 0.9821, Val Loss: 0.1262, Val Accuracy: 0.9489 Epoch 868/1000, Train Loss: 0.0550, Train Accuracy: 0.9842, Val Loss: 0.1264, Val Accuracy: 0.9484 Epoch 869/1000, Train Loss: 0.0555, Train Accuracy: 0.9827, Val Loss: 0.1264, Val Accuracy: 0.9489 Epoch 870/1000, Train Loss: 0.0550, Train Accuracy: 0.9831, Val Loss: 0.1261, Val Accuracy: 0.9499 Epoch 871/1000, Train Loss: 0.0568, Train Accuracy: 0.9832, Val Loss: 0.1260, Val Accuracy: 0.9494 Epoch 872/1000, Train Loss: 0.0563, Train Accuracy: 0.9848, Val Loss: 0.1262, Val Accuracy: 0.9499 Epoch 873/1000, Train Loss: 0.0544, Train Accuracy: 0.9856, Val Loss: 0.1262, Val Accuracy: 0.9499 Epoch 874/1000, Train Loss: 0.0556, Train Accuracy: 0.9833, Val Loss: 0.1262, Val Accuracy: 0.9494 Epoch 875/1000, Train Loss: 0.0565, Train Accuracy: 0.9831, Val Loss: 0.1261, Val Accuracy: 0.9494 Epoch 876/1000, Train Loss: 0.0548, Train Accuracy: 0.9849, Val Loss: 0.1261, Val Accuracy: 0.9494 Epoch 877/1000, Train Loss: 0.0575, Train Accuracy: 0.9827, Val Loss: 0.1260, Val Accuracy: 0.9489 Epoch 878/1000, Train Loss: 0.0539, Train Accuracy: 0.9836, Val Loss: 0.1259, Val Accuracy: 0.9494 Epoch 879/1000, Train Loss: 0.0569, Train Accuracy: 0.9825, Val Loss: 0.1260, Val Accuracy: 0.9499 Epoch 880/1000, Train Loss: 0.0539, Train Accuracy: 0.9860, Val Loss: 0.1261, Val Accuracy: 0.9499 Epoch 881/1000, Train Loss: 0.0539, Train Accuracy: 0.9838, Val Loss: 0.1262, Val Accuracy: 0.9494 Epoch 882/1000, Train Loss: 0.0543, Train Accuracy: 0.9841, Val Loss: 0.1261, Val Accuracy: 0.9499 Epoch 883/1000, Train Loss: 0.0544, Train Accuracy: 0.9850, Val Loss: 0.1260, Val Accuracy: 0.9499 Epoch 884/1000, Train Loss: 0.0535, Train Accuracy: 0.9854, Val Loss: 0.1260, Val Accuracy: 0.9504 Epoch 885/1000, Train Loss: 0.0532, Train Accuracy: 0.9856, Val Loss: 0.1260, Val Accuracy: 0.9504 Epoch 886/1000, Train Loss: 0.0553, Train Accuracy: 0.9835, Val Loss: 0.1260, Val Accuracy: 0.9494 Epoch 887/1000, Train Loss: 0.0509, Train Accuracy: 0.9853, Val Loss: 0.1258, Val Accuracy: 0.9504 Epoch 888/1000, Train Loss: 0.0531, Train Accuracy: 0.9844, Val Loss: 0.1256, Val Accuracy: 0.9504 Epoch 889/1000, Train Loss: 0.0535, Train Accuracy: 0.9848, Val Loss: 0.1256, Val Accuracy: 0.9499 Epoch 890/1000, Train Loss: 0.0540, Train Accuracy: 0.9843, Val Loss: 0.1255, Val Accuracy: 0.9504 Epoch 891/1000, Train Loss: 0.0527, Train Accuracy: 0.9855, Val Loss: 0.1255, Val Accuracy: 0.9509 Epoch 892/1000, Train Loss: 0.0522, Train Accuracy: 0.9855, Val Loss: 0.1254, Val Accuracy: 0.9509 Epoch 893/1000, Train Loss: 0.0554, Train Accuracy: 0.9841, Val Loss: 0.1255, Val Accuracy: 0.9504 Epoch 894/1000, Train Loss: 0.0526, Train Accuracy: 0.9846, Val Loss: 0.1256, Val Accuracy: 0.9504 Epoch 895/1000, Train Loss: 0.0523, Train Accuracy: 0.9852, Val Loss: 0.1257, Val Accuracy: 0.9504 Epoch 896/1000, Train Loss: 0.0531, Train Accuracy: 0.9839, Val Loss: 0.1255, Val Accuracy: 0.9499 Epoch 897/1000, Train Loss: 0.0538, Train Accuracy: 0.9849, Val Loss: 0.1256, Val Accuracy: 0.9504 Epoch 898/1000, Train Loss: 0.0542, Train Accuracy: 0.9837, Val Loss: 0.1258, Val Accuracy: 0.9504 Epoch 899/1000, Train Loss: 0.0512, Train Accuracy: 0.9859, Val Loss: 0.1260, Val Accuracy: 0.9499 Epoch 900/1000, Train Loss: 0.0510, Train Accuracy: 0.9847, Val Loss: 0.1255, Val Accuracy: 0.9509 Epoch 901/1000, Train Loss: 0.0513, Train Accuracy: 0.9852, Val Loss: 0.1254, Val Accuracy: 0.9514 Epoch 902/1000, Train Loss: 0.0508, Train Accuracy: 0.9855, Val Loss: 0.1254, Val Accuracy: 0.9514 Epoch 903/1000, Train Loss: 0.0521, Train Accuracy: 0.9852, Val Loss: 0.1253, Val Accuracy: 0.9514 Epoch 904/1000, Train Loss: 0.0489, Train Accuracy: 0.9882, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 905/1000, Train Loss: 0.0493, Train Accuracy: 0.9867, Val Loss: 0.1252, Val Accuracy: 0.9509 Epoch 906/1000, Train Loss: 0.0518, Train Accuracy: 0.9848, Val Loss: 0.1253, Val Accuracy: 0.9509 Epoch 907/1000, Train Loss: 0.0512, Train Accuracy: 0.9852, Val Loss: 0.1251, Val Accuracy: 0.9509 Epoch 908/1000, Train Loss: 0.0516, Train Accuracy: 0.9858, Val Loss: 0.1251, Val Accuracy: 0.9509 Epoch 909/1000, Train Loss: 0.0535, Train Accuracy: 0.9843, Val Loss: 0.1251, Val Accuracy: 0.9504 Epoch 910/1000, Train Loss: 0.0514, Train Accuracy: 0.9844, Val Loss: 0.1252, Val Accuracy: 0.9509 Epoch 911/1000, Train Loss: 0.0499, Train Accuracy: 0.9863, Val Loss: 0.1249, Val Accuracy: 0.9509 Epoch 912/1000, Train Loss: 0.0513, Train Accuracy: 0.9849, Val Loss: 0.1249, Val Accuracy: 0.9504 Epoch 913/1000, Train Loss: 0.0512, Train Accuracy: 0.9842, Val Loss: 0.1251, Val Accuracy: 0.9514 Epoch 914/1000, Train Loss: 0.0497, Train Accuracy: 0.9849, Val Loss: 0.1250, Val Accuracy: 0.9514 Epoch 915/1000, Train Loss: 0.0505, Train Accuracy: 0.9855, Val Loss: 0.1250, Val Accuracy: 0.9518 Epoch 916/1000, Train Loss: 0.0508, Train Accuracy: 0.9855, Val Loss: 0.1249, Val Accuracy: 0.9509 Epoch 917/1000, Train Loss: 0.0519, Train Accuracy: 0.9858, Val Loss: 0.1249, Val Accuracy: 0.9514 Epoch 918/1000, Train Loss: 0.0510, Train Accuracy: 0.9859, Val Loss: 0.1250, Val Accuracy: 0.9514 Epoch 919/1000, Train Loss: 0.0495, Train Accuracy: 0.9852, Val Loss: 0.1251, Val Accuracy: 0.9509 Epoch 920/1000, Train Loss: 0.0514, Train Accuracy: 0.9837, Val Loss: 0.1248, Val Accuracy: 0.9514 Epoch 921/1000, Train Loss: 0.0498, Train Accuracy: 0.9856, Val Loss: 0.1250, Val Accuracy: 0.9514 Epoch 922/1000, Train Loss: 0.0485, Train Accuracy: 0.9870, Val Loss: 0.1250, Val Accuracy: 0.9509 Epoch 923/1000, Train Loss: 0.0507, Train Accuracy: 0.9842, Val Loss: 0.1249, Val Accuracy: 0.9518 Epoch 924/1000, Train Loss: 0.0488, Train Accuracy: 0.9856, Val Loss: 0.1250, Val Accuracy: 0.9514 Epoch 925/1000, Train Loss: 0.0484, Train Accuracy: 0.9858, Val Loss: 0.1250, Val Accuracy: 0.9504 Epoch 926/1000, Train Loss: 0.0480, Train Accuracy: 0.9881, Val Loss: 0.1251, Val Accuracy: 0.9509 Epoch 927/1000, Train Loss: 0.0481, Train Accuracy: 0.9880, Val Loss: 0.1250, Val Accuracy: 0.9514 Epoch 928/1000, Train Loss: 0.0468, Train Accuracy: 0.9873, Val Loss: 0.1250, Val Accuracy: 0.9514 Epoch 929/1000, Train Loss: 0.0500, Train Accuracy: 0.9853, Val Loss: 0.1251, Val Accuracy: 0.9514 Epoch 930/1000, Train Loss: 0.0504, Train Accuracy: 0.9856, Val Loss: 0.1250, Val Accuracy: 0.9509 Epoch 931/1000, Train Loss: 0.0476, Train Accuracy: 0.9875, Val Loss: 0.1253, Val Accuracy: 0.9509 Epoch 932/1000, Train Loss: 0.0475, Train Accuracy: 0.9878, Val Loss: 0.1252, Val Accuracy: 0.9509 Epoch 933/1000, Train Loss: 0.0458, Train Accuracy: 0.9883, Val Loss: 0.1253, Val Accuracy: 0.9514 Epoch 934/1000, Train Loss: 0.0460, Train Accuracy: 0.9876, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 935/1000, Train Loss: 0.0478, Train Accuracy: 0.9864, Val Loss: 0.1253, Val Accuracy: 0.9509 Epoch 936/1000, Train Loss: 0.0464, Train Accuracy: 0.9866, Val Loss: 0.1252, Val Accuracy: 0.9509 Epoch 937/1000, Train Loss: 0.0481, Train Accuracy: 0.9855, Val Loss: 0.1253, Val Accuracy: 0.9509 Epoch 938/1000, Train Loss: 0.0443, Train Accuracy: 0.9883, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 939/1000, Train Loss: 0.0477, Train Accuracy: 0.9867, Val Loss: 0.1250, Val Accuracy: 0.9514 Epoch 940/1000, Train Loss: 0.0492, Train Accuracy: 0.9863, Val Loss: 0.1248, Val Accuracy: 0.9523 Epoch 941/1000, Train Loss: 0.0452, Train Accuracy: 0.9888, Val Loss: 0.1247, Val Accuracy: 0.9523 Epoch 942/1000, Train Loss: 0.0469, Train Accuracy: 0.9870, Val Loss: 0.1248, Val Accuracy: 0.9518 Epoch 943/1000, Train Loss: 0.0469, Train Accuracy: 0.9856, Val Loss: 0.1248, Val Accuracy: 0.9518 Epoch 944/1000, Train Loss: 0.0469, Train Accuracy: 0.9882, Val Loss: 0.1248, Val Accuracy: 0.9518 Epoch 945/1000, Train Loss: 0.0447, Train Accuracy: 0.9877, Val Loss: 0.1249, Val Accuracy: 0.9518 Epoch 946/1000, Train Loss: 0.0494, Train Accuracy: 0.9853, Val Loss: 0.1248, Val Accuracy: 0.9518 Epoch 947/1000, Train Loss: 0.0482, Train Accuracy: 0.9853, Val Loss: 0.1246, Val Accuracy: 0.9523 Epoch 948/1000, Train Loss: 0.0442, Train Accuracy: 0.9892, Val Loss: 0.1246, Val Accuracy: 0.9523 Epoch 949/1000, Train Loss: 0.0447, Train Accuracy: 0.9878, Val Loss: 0.1248, Val Accuracy: 0.9523 Epoch 950/1000, Train Loss: 0.0460, Train Accuracy: 0.9876, Val Loss: 0.1249, Val Accuracy: 0.9528 Epoch 951/1000, Train Loss: 0.0454, Train Accuracy: 0.9880, Val Loss: 0.1248, Val Accuracy: 0.9528 Epoch 952/1000, Train Loss: 0.0472, Train Accuracy: 0.9875, Val Loss: 0.1249, Val Accuracy: 0.9523 Epoch 953/1000, Train Loss: 0.0464, Train Accuracy: 0.9869, Val Loss: 0.1252, Val Accuracy: 0.9518 Epoch 954/1000, Train Loss: 0.0481, Train Accuracy: 0.9865, Val Loss: 0.1250, Val Accuracy: 0.9528 Epoch 955/1000, Train Loss: 0.0469, Train Accuracy: 0.9866, Val Loss: 0.1251, Val Accuracy: 0.9523 Epoch 956/1000, Train Loss: 0.0427, Train Accuracy: 0.9891, Val Loss: 0.1251, Val Accuracy: 0.9528 Epoch 957/1000, Train Loss: 0.0451, Train Accuracy: 0.9881, Val Loss: 0.1251, Val Accuracy: 0.9518 Epoch 958/1000, Train Loss: 0.0444, Train Accuracy: 0.9884, Val Loss: 0.1254, Val Accuracy: 0.9509 Epoch 959/1000, Train Loss: 0.0453, Train Accuracy: 0.9887, Val Loss: 0.1253, Val Accuracy: 0.9509 Epoch 960/1000, Train Loss: 0.0465, Train Accuracy: 0.9858, Val Loss: 0.1254, Val Accuracy: 0.9514 Epoch 961/1000, Train Loss: 0.0446, Train Accuracy: 0.9875, Val Loss: 0.1253, Val Accuracy: 0.9518 Epoch 962/1000, Train Loss: 0.0422, Train Accuracy: 0.9881, Val Loss: 0.1252, Val Accuracy: 0.9523 Epoch 963/1000, Train Loss: 0.0444, Train Accuracy: 0.9876, Val Loss: 0.1252, Val Accuracy: 0.9523 Epoch 964/1000, Train Loss: 0.0423, Train Accuracy: 0.9884, Val Loss: 0.1252, Val Accuracy: 0.9523 Epoch 965/1000, Train Loss: 0.0423, Train Accuracy: 0.9881, Val Loss: 0.1250, Val Accuracy: 0.9523 Epoch 966/1000, Train Loss: 0.0437, Train Accuracy: 0.9878, Val Loss: 0.1252, Val Accuracy: 0.9518 Epoch 967/1000, Train Loss: 0.0446, Train Accuracy: 0.9880, Val Loss: 0.1254, Val Accuracy: 0.9518 Epoch 968/1000, Train Loss: 0.0437, Train Accuracy: 0.9866, Val Loss: 0.1255, Val Accuracy: 0.9518 Epoch 969/1000, Train Loss: 0.0441, Train Accuracy: 0.9882, Val Loss: 0.1255, Val Accuracy: 0.9514 Epoch 970/1000, Train Loss: 0.0433, Train Accuracy: 0.9878, Val Loss: 0.1256, Val Accuracy: 0.9514 Epoch 971/1000, Train Loss: 0.0445, Train Accuracy: 0.9863, Val Loss: 0.1256, Val Accuracy: 0.9514 Epoch 972/1000, Train Loss: 0.0420, Train Accuracy: 0.9889, Val Loss: 0.1253, Val Accuracy: 0.9514 Epoch 973/1000, Train Loss: 0.0430, Train Accuracy: 0.9881, Val Loss: 0.1253, Val Accuracy: 0.9514 Epoch 974/1000, Train Loss: 0.0437, Train Accuracy: 0.9881, Val Loss: 0.1252, Val Accuracy: 0.9518 Epoch 975/1000, Train Loss: 0.0430, Train Accuracy: 0.9891, Val Loss: 0.1251, Val Accuracy: 0.9514 Epoch 976/1000, Train Loss: 0.0426, Train Accuracy: 0.9891, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 977/1000, Train Loss: 0.0418, Train Accuracy: 0.9884, Val Loss: 0.1253, Val Accuracy: 0.9514 Epoch 978/1000, Train Loss: 0.0416, Train Accuracy: 0.9886, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 979/1000, Train Loss: 0.0419, Train Accuracy: 0.9877, Val Loss: 0.1254, Val Accuracy: 0.9514 Epoch 980/1000, Train Loss: 0.0458, Train Accuracy: 0.9865, Val Loss: 0.1253, Val Accuracy: 0.9514 Epoch 981/1000, Train Loss: 0.0424, Train Accuracy: 0.9898, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 982/1000, Train Loss: 0.0421, Train Accuracy: 0.9882, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 983/1000, Train Loss: 0.0432, Train Accuracy: 0.9870, Val Loss: 0.1253, Val Accuracy: 0.9518 Epoch 984/1000, Train Loss: 0.0432, Train Accuracy: 0.9869, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 985/1000, Train Loss: 0.0403, Train Accuracy: 0.9889, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 986/1000, Train Loss: 0.0409, Train Accuracy: 0.9878, Val Loss: 0.1254, Val Accuracy: 0.9514 Epoch 987/1000, Train Loss: 0.0394, Train Accuracy: 0.9908, Val Loss: 0.1254, Val Accuracy: 0.9514 Epoch 988/1000, Train Loss: 0.0405, Train Accuracy: 0.9895, Val Loss: 0.1254, Val Accuracy: 0.9514 Epoch 989/1000, Train Loss: 0.0420, Train Accuracy: 0.9892, Val Loss: 0.1251, Val Accuracy: 0.9518 Epoch 990/1000, Train Loss: 0.0427, Train Accuracy: 0.9882, Val Loss: 0.1252, Val Accuracy: 0.9518 Epoch 991/1000, Train Loss: 0.0419, Train Accuracy: 0.9887, Val Loss: 0.1248, Val Accuracy: 0.9518 Epoch 992/1000, Train Loss: 0.0404, Train Accuracy: 0.9887, Val Loss: 0.1249, Val Accuracy: 0.9523 Epoch 993/1000, Train Loss: 0.0391, Train Accuracy: 0.9906, Val Loss: 0.1249, Val Accuracy: 0.9523 Epoch 994/1000, Train Loss: 0.0410, Train Accuracy: 0.9891, Val Loss: 0.1252, Val Accuracy: 0.9523 Epoch 995/1000, Train Loss: 0.0396, Train Accuracy: 0.9897, Val Loss: 0.1251, Val Accuracy: 0.9523 Epoch 996/1000, Train Loss: 0.0417, Train Accuracy: 0.9884, Val Loss: 0.1254, Val Accuracy: 0.9523 Epoch 997/1000, Train Loss: 0.0394, Train Accuracy: 0.9901, Val Loss: 0.1254, Val Accuracy: 0.9514 Epoch 998/1000, Train Loss: 0.0389, Train Accuracy: 0.9883, Val Loss: 0.1254, Val Accuracy: 0.9514 Epoch 999/1000, Train Loss: 0.0403, Train Accuracy: 0.9897, Val Loss: 0.1255, Val Accuracy: 0.9514 Epoch 1000/1000, Train Loss: 0.0395, Train Accuracy: 0.9888, Val Loss: 0.1253, Val Accuracy: 0.9518
# Create the neural network classifier
clf = MLPClassifier(random_state=42)
# Assuming you have performed a grid search and obtained the best estimator
# Define the parameter grid
param_grid = {
'hidden_layer_sizes': [(30,30), (10,10,10)],
'solver': ['adam', 'sgd'],
'alpha': [0.001, 0.01],
}
# Create the GridSearchCV object
grid_search = GridSearchCV(clf, param_grid, scoring='accuracy', cv=2)
# Fit the GridSearchCV object to the data
grid_search.fit(X_train_DS_HCC1806_nn, y_train_DS_HCC1806_nn)
# Save the best estimator in best_nn
best_nn_DS_HCC1806 = grid_search.best_estimator_
print(best_nn_DS_HCC1806)
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn(
MLPClassifier(alpha=0.01, hidden_layer_sizes=(30, 30), random_state=42)
torch.save(model_DS_H.state_dict(), best_model_path_H)
¶
Test for the model drop-seq HCC1806
This below is the step for testing our neural network with HCC1806 dataset.
As before the model is set to evaluation mode, and test data is converted to PyTorch tensors.
A forward pass is performed on the test data to obtain predictions. The test loss is calculated using the same loss function as before and predictions are converted to binary values by applying a sigmoid function followed by rounding. Finally, The number of correct predictions is counted and used to compute the test accuracy.
# Load the best model
model_DS_H.load_state_dict(torch.load('best_model_final_H.pth'))
# Evaluate on test data
model_DS_H.eval()
X_test_tensor_H = torch.tensor(X_test_df_DS_HCC1806.values, dtype=torch.float32)
y_test_tensor_H = torch.tensor(y_test_df_DS_HCC1806.values, dtype=torch.float32).unsqueeze(1)
with torch.no_grad():
test_output = model_DS_H(X_test_tensor_H)
test_loss = criterion(test_output, y_test_tensor_H)
test_predictions = torch.round(torch.sigmoid(test_output))
test_correct = (test_predictions == y_test_tensor_H).sum().item()
test_total = y_test_tensor_H.size(0)
test_acc = test_correct / test_total
print(f'Test Loss: {test_loss.item():.4f}, Test Accuracy: {test_acc:.4f}')
Test Loss: 0.1195, Test Accuracy: 0.9544
¶
4.4.5) KNN
best_knn_DS_HCC1806=train_and_evaluate_knn(X_train_DS_HCC, X_test_DS_HCC, y_train_DS_HCC, y_test_DS_HCC)
Best hyperparameters: {'n_neighbors': 3}
Accuracy: 0.47020769492679604
F1: 0.30035971223021585
Classification Report:
precision recall f1-score support
0.0 0.42 0.89 0.57 1171
1.0 0.73 0.19 0.30 1766
accuracy 0.47 2937
macro avg 0.58 0.54 0.44 2937
weighted avg 0.61 0.47 0.41 2937
¶
4.4.6) Logistic Regression
best_logreg_DS_HCC1806=train_and_evaluate_logreg(X_train_DS_HCC, X_test_DS_HCC, y_train_DS_HCC, y_test_DS_HCC)
Best hyperparameters: {'C': 0.002, 'penalty': 'l2'}
Accuracy: 0.9482465100442629
F1: 0.9565962307252998
Classification Report:
precision recall f1-score support
0.0 0.92 0.95 0.94 1171
1.0 0.96 0.95 0.96 1766
accuracy 0.95 2937
macro avg 0.94 0.95 0.95 2937
weighted avg 0.95 0.95 0.95 2937
# try pca method to reduce number of features and use log log regression
X_train_pca_DS_HCC, X_test_pca_DS_HCC, y_train_DS_HCC, y_test_DS_HCC,scaler_DS_HCC,pca_DS_HCC = prepare_data_pca(df_trans_DS_HCC)
X_train_pca_DS_HCC.shape
(11745, 1760)
model_logreg_hcc_DROP_ver2_DS_HCC=train_and_evaluate_logreg(X_train_pca_DS_HCC, X_test_pca_DS_HCC, y_train_DS_HCC, y_test_DS_HCC)
Best hyperparameters: {'C': 0.002, 'penalty': 'l2'}
Accuracy: 0.9472250595846101
F1: 0.955953395851094
Classification Report:
precision recall f1-score support
0.0 0.93 0.94 0.93 1171
1.0 0.96 0.95 0.96 1766
accuracy 0.95 2937
macro avg 0.94 0.95 0.95 2937
weighted avg 0.95 0.95 0.95 2937
¶
4.4.7) CatBoost
best_catboost_DS_HCC1806=train_and_evaluate_catboost(X_train_DS_HCC, X_test_DS_HCC, y_train_DS_HCC, y_test_DS_HCC)
Best hyperparameters: {'logging_level': 'Silent'}
Accuracy: 0.9564181137214846
F1: 0.9637188208616779
Classification Report:
precision recall f1-score support
0.0 0.94 0.95 0.95 1171
1.0 0.96 0.96 0.96 1766
accuracy 0.96 2937
macro avg 0.95 0.95 0.95 2937
weighted avg 0.96 0.96 0.96 2937
new_columns_names_DS_HCC = []
for i in range(1,3001):
name_new_DS_HCC = f'f{i}'
new_columns_names_DS_HCC.append(name_new_DS_HCC)
X_test_pd_DS_HCC=pd.DataFrame(X_test_DS_HCC, columns=new_columns_names_DS_HCC)
X_train_pd_DS_HCC=pd.DataFrame(X_train_DS_HCC, columns=new_columns_names_DS_HCC)
model_catboost_hcc_DROP_ver2_DS_HCC,cols_cut_DS_HCC=train_and_evaluate_catboost2(X_train_pd_DS_HCC, X_test_pd_DS_HCC, y_train_DS_HCC, y_test_DS_HCC)
229
Best hyperparameters: {'l2_leaf_reg': 6, 'logging_level': 'Silent', 'n_estimators': 1500}
Accuracy: 0.9605039155600953
F1: 0.966989186112692
Classification Report:
precision recall f1-score support
0.0 0.94 0.96 0.95 1171
1.0 0.97 0.96 0.97 1766
accuracy 0.96 2937
macro avg 0.96 0.96 0.96 2937
weighted avg 0.96 0.96 0.96 2937
¶
4.5) PREDICTORS ON DROPSEQ MERGED (Neural Network)
# Merge the two DataFrames with suffixes for overlapping columns
DS_merged = df_DS_MCF7_norm_filtT.T.join(df_DS_HCC1806_norm_filtT.T, how="outer", lsuffix='_MCF7', rsuffix='_HCC1806')
DS_merged_complete = DS_merged.fillna(0) # Replace NaN values with 0
DS_merged_complete
| "AAAAACCTATCG_Normoxia" | "AAAACAACCCTA_Normoxia" | "AAAACACTCTCA_Normoxia" | "AAAACCAGGCAC_Normoxia" | "AAAACCTAGCTC_Normoxia" | "AAAACCTCCGGG_Normoxia" | "AAAACTCGTTGC_Normoxia" | "AAAAGAGCTCTC_Normoxia" | "AAAAGCTAGGCG_Normoxia" | "AAAATCGCATTT_Normoxia" | ... | "TTTTCTGATGGT_Hypoxia" | "TTTTGATTCAGA_Hypoxia" | "TTTTGCAACTGA_Hypoxia" | "TTTTGCCGGGCC_Hypoxia" | "TTTTGTTAGCCT_Hypoxia" | "TTTTTACCAATC_Hypoxia" | "TTTTTCCGTGCA_Hypoxia" | "TTTTTGCCTGGG_Hypoxia" | "TTTTTGTAACAG_Hypoxia" | "TTTTTTTGAATC_Hypoxia" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| "A4GALT" | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| "AAAS" | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| "AAGAB" | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| "AAMDC" | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| "AAMP" | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| "ZYG11A" | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| "ZYX" | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| "ZZEF1" | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| "ZZZ3" | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| target | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
5167 rows × 36308 columns
We now define the neural network model to be used with the merged datasets dropseq MCF7 and HCC1806
model_merged_DS = NeuralNetworkDropSeqMerged()
model_merged_DS
NeuralNetworkDropSeqMerged(
(model): Sequential(
(0): Flatten(start_dim=1, end_dim=-1)
(1): Linear(in_features=5166, out_features=512, bias=True)
(2): LeakyReLU(negative_slope=0.01)
(3): Dropout(p=0.4, inplace=False)
(4): Linear(in_features=512, out_features=256, bias=True)
(5): LeakyReLU(negative_slope=0.01)
(6): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): Dropout(p=0.4, inplace=False)
(8): Linear(in_features=256, out_features=128, bias=True)
(9): LeakyReLU(negative_slope=0.01)
(10): Dropout(p=0.3, inplace=False)
(11): Linear(in_features=128, out_features=64, bias=True)
(12): LeakyReLU(negative_slope=0.01)
(13): Linear(in_features=64, out_features=1, bias=True)
)
)
DS_merged_complete_T = DS_merged_complete.T
DS_merged_complete_T.shape
(36308, 5167)
We split the dataset and labels into train/test dataset and labels, and then we split again the train dataset into train one and the validation one. Then we convert these variables into tensors in order to be able to use them with pytorch.
# Exclude the column using its name
# Exclude the column using its name
X_merged = DS_merged_complete_T.iloc[:, :-1].values
y_merged = DS_merged_complete_T.iloc[:, -1].values
# Train/Test Split (starting with 80% train, 20% test)
X_train_merged, X_test_merged, y_train_merged, y_test_merged = train_test_split(X_merged, y_merged, test_size=0.2, random_state=42)
# We do an addition splitting of the training set into the training set and a validation set (useful to tune the hyperparameters)
X_train_merged_nn, X_val_merged_nn, y_train_merged_nn, y_val_merged_nn = train_test_split(X_train_merged, y_train_merged, test_size=0.2, random_state=42)
# Transform them into tensors
X_train_tensor_merged = torch.tensor(X_train_merged_nn, dtype=torch.float32)
y_train_tensor_merged = torch.tensor(y_train_merged_nn, dtype=torch.float32).unsqueeze(1) # For binary classification
X_val_tensor_merged = torch.tensor(X_val_merged_nn, dtype=torch.float32)
y_val_tensor_merged = torch.tensor(y_val_merged_nn, dtype=torch.float32).unsqueeze(1) # For binary classification
X_val_tensor_merged
tensor([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
¶
Training for the model drop-seq merged
Here below there is the training process of the neural network using the merged dataset.
It follows the same structure of the training process used with the individual MCF7 and HCC1806 datasets; therefore we use Stochastic Gradient Descent (SGD) as the optimizer with a learning rate of 0.01, and asa loss function the binary cross-entropy with logits ('BCEWithLogitsLoss'). Also here, the training runs for 1000 epochs.
The training loop starts by setting the model to training mode, resetting the gradients, performing a forward pass on the training data, calculating the training loss, and then using backpropagation to compute the gradients. The optimizer updates the model parameters based on these gradients. Finally, training accuracy is calculated by rounding the sigmoid-activated predictions and comparing them to the true labels.
For validation, the model is set to evaluation mode, and a forward pass is performed on the validation data. The validation loss and accuracy are calculated similarly to the training process but without computing gradients.
optimizer = torch.optim.SGD(model_merged_DS.parameters(), lr=0.01)
criterion = nn.BCEWithLogitsLoss() # applies a sigmoid activation to the model's raw logits and calculates the binary cross-entropy loss
epochs = 1000
best_val_acc = 0
patience = 3
no_improve_epochs = 0
best_model_path_merged = 'best_model_final_merged.pth'
for epoch in range(epochs):
model_merged_DS.train()
optimizer.zero_grad() # resets the gradients of all model parameters to zero.
train_output = model_merged_DS(X_train_tensor_merged) # Forward pass with all training data, computes the model's predictions (output) for the entire training dataset
train_loss = criterion(train_output, y_train_tensor_merged) # calculates the difference between predicted outputs and actual labels
train_loss.backward() #computes the gradients of the loss with respect to the model's parameters
# This uses backpropagation to distribute the error back through the network
optimizer.step() #The optimizer applies the selected algorithm (like SGD) to update the weights
# Calculate training accuracy
with torch.no_grad():
train_predictions = torch.round(torch.sigmoid(train_output))
train_correct = (train_predictions == y_train_tensor_merged).sum().item()
train_total = y_train_tensor_merged.size(0)
train_acc = train_correct / train_total
# Validation
model_merged_DS.eval() # sets the model to evaluation mode, disabling features like dropout and batch normalization to ensure consistency
with torch.no_grad(): #prevents PyTorch from computing gradients during evaluation.
val_output = model_merged_DS(X_val_tensor_merged) #computes predictions on the validation dataset
val_loss = criterion(val_output, y_val_tensor_merged)
val_predictions = torch.round(torch.sigmoid(val_output)) # converts the logits/predictions into binary predictions (0 or 1)
val_correct = (val_predictions == y_val_tensor_merged).sum().item()
val_total = y_val_tensor_merged.size(0)
val_acc = val_correct / val_total
print(f'Epoch {epoch+1}/{epochs}, '
f'Train Loss: {train_loss.item():.4f}, '
f'Train Accuracy: {train_acc:.4f}, '
f'Val Loss: {val_loss.item():.4f}, '
f'Val Accuracy: {val_acc:.4f}')
# Save model after all epochs are completed
#torch.save(model.state_dict(), best_model_path)
Epoch 1/1000, Train Loss: 0.6990, Train Accuracy: 0.4920, Val Loss: 0.6973, Val Accuracy: 0.4873 Epoch 2/1000, Train Loss: 0.6982, Train Accuracy: 0.4918, Val Loss: 0.6972, Val Accuracy: 0.4873 Epoch 3/1000, Train Loss: 0.6975, Train Accuracy: 0.4930, Val Loss: 0.6972, Val Accuracy: 0.4873 Epoch 4/1000, Train Loss: 0.6971, Train Accuracy: 0.4932, Val Loss: 0.6971, Val Accuracy: 0.4873 Epoch 5/1000, Train Loss: 0.6959, Train Accuracy: 0.4947, Val Loss: 0.6970, Val Accuracy: 0.4873 Epoch 6/1000, Train Loss: 0.6951, Train Accuracy: 0.4940, Val Loss: 0.6969, Val Accuracy: 0.4873 Epoch 7/1000, Train Loss: 0.6939, Train Accuracy: 0.4967, Val Loss: 0.6968, Val Accuracy: 0.4873 Epoch 8/1000, Train Loss: 0.6935, Train Accuracy: 0.4958, Val Loss: 0.6968, Val Accuracy: 0.4873 Epoch 9/1000, Train Loss: 0.6924, Train Accuracy: 0.4963, Val Loss: 0.6967, Val Accuracy: 0.4873 Epoch 10/1000, Train Loss: 0.6920, Train Accuracy: 0.4977, Val Loss: 0.6966, Val Accuracy: 0.4873 Epoch 11/1000, Train Loss: 0.6916, Train Accuracy: 0.4985, Val Loss: 0.6965, Val Accuracy: 0.4873 Epoch 12/1000, Train Loss: 0.6914, Train Accuracy: 0.4977, Val Loss: 0.6964, Val Accuracy: 0.4873 Epoch 13/1000, Train Loss: 0.6895, Train Accuracy: 0.4999, Val Loss: 0.6963, Val Accuracy: 0.4873 Epoch 14/1000, Train Loss: 0.6890, Train Accuracy: 0.5001, Val Loss: 0.6961, Val Accuracy: 0.4873 Epoch 15/1000, Train Loss: 0.6880, Train Accuracy: 0.5015, Val Loss: 0.6960, Val Accuracy: 0.4873 Epoch 16/1000, Train Loss: 0.6879, Train Accuracy: 0.5026, Val Loss: 0.6959, Val Accuracy: 0.4873 Epoch 17/1000, Train Loss: 0.6867, Train Accuracy: 0.5049, Val Loss: 0.6957, Val Accuracy: 0.4873 Epoch 18/1000, Train Loss: 0.6860, Train Accuracy: 0.5043, Val Loss: 0.6955, Val Accuracy: 0.4873 Epoch 19/1000, Train Loss: 0.6859, Train Accuracy: 0.5036, Val Loss: 0.6954, Val Accuracy: 0.4873 Epoch 20/1000, Train Loss: 0.6848, Train Accuracy: 0.5080, Val Loss: 0.6952, Val Accuracy: 0.4873 Epoch 21/1000, Train Loss: 0.6840, Train Accuracy: 0.5082, Val Loss: 0.6950, Val Accuracy: 0.4873 Epoch 22/1000, Train Loss: 0.6829, Train Accuracy: 0.5095, Val Loss: 0.6948, Val Accuracy: 0.4873 Epoch 23/1000, Train Loss: 0.6830, Train Accuracy: 0.5106, Val Loss: 0.6945, Val Accuracy: 0.4873 Epoch 24/1000, Train Loss: 0.6816, Train Accuracy: 0.5134, Val Loss: 0.6943, Val Accuracy: 0.4871 Epoch 25/1000, Train Loss: 0.6815, Train Accuracy: 0.5127, Val Loss: 0.6940, Val Accuracy: 0.4871 Epoch 26/1000, Train Loss: 0.6800, Train Accuracy: 0.5192, Val Loss: 0.6938, Val Accuracy: 0.4867 Epoch 27/1000, Train Loss: 0.6793, Train Accuracy: 0.5190, Val Loss: 0.6934, Val Accuracy: 0.4867 Epoch 28/1000, Train Loss: 0.6788, Train Accuracy: 0.5226, Val Loss: 0.6931, Val Accuracy: 0.4867 Epoch 29/1000, Train Loss: 0.6784, Train Accuracy: 0.5192, Val Loss: 0.6928, Val Accuracy: 0.4867 Epoch 30/1000, Train Loss: 0.6775, Train Accuracy: 0.5245, Val Loss: 0.6924, Val Accuracy: 0.4867 Epoch 31/1000, Train Loss: 0.6768, Train Accuracy: 0.5277, Val Loss: 0.6920, Val Accuracy: 0.4864 Epoch 32/1000, Train Loss: 0.6758, Train Accuracy: 0.5299, Val Loss: 0.6915, Val Accuracy: 0.4864 Epoch 33/1000, Train Loss: 0.6751, Train Accuracy: 0.5279, Val Loss: 0.6911, Val Accuracy: 0.4862 Epoch 34/1000, Train Loss: 0.6741, Train Accuracy: 0.5335, Val Loss: 0.6906, Val Accuracy: 0.4861 Epoch 35/1000, Train Loss: 0.6743, Train Accuracy: 0.5353, Val Loss: 0.6900, Val Accuracy: 0.4861 Epoch 36/1000, Train Loss: 0.6736, Train Accuracy: 0.5385, Val Loss: 0.6895, Val Accuracy: 0.4866 Epoch 37/1000, Train Loss: 0.6723, Train Accuracy: 0.5437, Val Loss: 0.6889, Val Accuracy: 0.4871 Epoch 38/1000, Train Loss: 0.6714, Train Accuracy: 0.5458, Val Loss: 0.6882, Val Accuracy: 0.4874 Epoch 39/1000, Train Loss: 0.6709, Train Accuracy: 0.5450, Val Loss: 0.6875, Val Accuracy: 0.4876 Epoch 40/1000, Train Loss: 0.6699, Train Accuracy: 0.5501, Val Loss: 0.6868, Val Accuracy: 0.4890 Epoch 41/1000, Train Loss: 0.6688, Train Accuracy: 0.5525, Val Loss: 0.6860, Val Accuracy: 0.4897 Epoch 42/1000, Train Loss: 0.6682, Train Accuracy: 0.5532, Val Loss: 0.6852, Val Accuracy: 0.4907 Epoch 43/1000, Train Loss: 0.6683, Train Accuracy: 0.5566, Val Loss: 0.6843, Val Accuracy: 0.4926 Epoch 44/1000, Train Loss: 0.6671, Train Accuracy: 0.5603, Val Loss: 0.6834, Val Accuracy: 0.4941 Epoch 45/1000, Train Loss: 0.6666, Train Accuracy: 0.5609, Val Loss: 0.6824, Val Accuracy: 0.4964 Epoch 46/1000, Train Loss: 0.6652, Train Accuracy: 0.5666, Val Loss: 0.6814, Val Accuracy: 0.4972 Epoch 47/1000, Train Loss: 0.6650, Train Accuracy: 0.5691, Val Loss: 0.6804, Val Accuracy: 0.4988 Epoch 48/1000, Train Loss: 0.6642, Train Accuracy: 0.5721, Val Loss: 0.6793, Val Accuracy: 0.5026 Epoch 49/1000, Train Loss: 0.6635, Train Accuracy: 0.5716, Val Loss: 0.6781, Val Accuracy: 0.5052 Epoch 50/1000, Train Loss: 0.6622, Train Accuracy: 0.5768, Val Loss: 0.6770, Val Accuracy: 0.5084 Epoch 51/1000, Train Loss: 0.6619, Train Accuracy: 0.5775, Val Loss: 0.6758, Val Accuracy: 0.5134 Epoch 52/1000, Train Loss: 0.6605, Train Accuracy: 0.5835, Val Loss: 0.6745, Val Accuracy: 0.5174 Epoch 53/1000, Train Loss: 0.6602, Train Accuracy: 0.5848, Val Loss: 0.6732, Val Accuracy: 0.5215 Epoch 54/1000, Train Loss: 0.6591, Train Accuracy: 0.5905, Val Loss: 0.6719, Val Accuracy: 0.5243 Epoch 55/1000, Train Loss: 0.6585, Train Accuracy: 0.5930, Val Loss: 0.6705, Val Accuracy: 0.5299 Epoch 56/1000, Train Loss: 0.6578, Train Accuracy: 0.5966, Val Loss: 0.6692, Val Accuracy: 0.5344 Epoch 57/1000, Train Loss: 0.6566, Train Accuracy: 0.5989, Val Loss: 0.6678, Val Accuracy: 0.5408 Epoch 58/1000, Train Loss: 0.6560, Train Accuracy: 0.6046, Val Loss: 0.6664, Val Accuracy: 0.5472 Epoch 59/1000, Train Loss: 0.6550, Train Accuracy: 0.6051, Val Loss: 0.6650, Val Accuracy: 0.5530 Epoch 60/1000, Train Loss: 0.6543, Train Accuracy: 0.6064, Val Loss: 0.6635, Val Accuracy: 0.5577 Epoch 61/1000, Train Loss: 0.6534, Train Accuracy: 0.6146, Val Loss: 0.6621, Val Accuracy: 0.5637 Epoch 62/1000, Train Loss: 0.6529, Train Accuracy: 0.6143, Val Loss: 0.6606, Val Accuracy: 0.5716 Epoch 63/1000, Train Loss: 0.6526, Train Accuracy: 0.6176, Val Loss: 0.6591, Val Accuracy: 0.5806 Epoch 64/1000, Train Loss: 0.6504, Train Accuracy: 0.6234, Val Loss: 0.6577, Val Accuracy: 0.5862 Epoch 65/1000, Train Loss: 0.6497, Train Accuracy: 0.6239, Val Loss: 0.6562, Val Accuracy: 0.5935 Epoch 66/1000, Train Loss: 0.6493, Train Accuracy: 0.6240, Val Loss: 0.6548, Val Accuracy: 0.5990 Epoch 67/1000, Train Loss: 0.6484, Train Accuracy: 0.6268, Val Loss: 0.6534, Val Accuracy: 0.6060 Epoch 68/1000, Train Loss: 0.6480, Train Accuracy: 0.6336, Val Loss: 0.6519, Val Accuracy: 0.6105 Epoch 69/1000, Train Loss: 0.6463, Train Accuracy: 0.6378, Val Loss: 0.6505, Val Accuracy: 0.6157 Epoch 70/1000, Train Loss: 0.6453, Train Accuracy: 0.6373, Val Loss: 0.6491, Val Accuracy: 0.6217 Epoch 71/1000, Train Loss: 0.6450, Train Accuracy: 0.6419, Val Loss: 0.6477, Val Accuracy: 0.6299 Epoch 72/1000, Train Loss: 0.6435, Train Accuracy: 0.6493, Val Loss: 0.6463, Val Accuracy: 0.6351 Epoch 73/1000, Train Loss: 0.6428, Train Accuracy: 0.6519, Val Loss: 0.6449, Val Accuracy: 0.6423 Epoch 74/1000, Train Loss: 0.6417, Train Accuracy: 0.6528, Val Loss: 0.6435, Val Accuracy: 0.6504 Epoch 75/1000, Train Loss: 0.6408, Train Accuracy: 0.6592, Val Loss: 0.6422, Val Accuracy: 0.6578 Epoch 76/1000, Train Loss: 0.6396, Train Accuracy: 0.6617, Val Loss: 0.6409, Val Accuracy: 0.6635 Epoch 77/1000, Train Loss: 0.6390, Train Accuracy: 0.6631, Val Loss: 0.6395, Val Accuracy: 0.6682 Epoch 78/1000, Train Loss: 0.6379, Train Accuracy: 0.6668, Val Loss: 0.6382, Val Accuracy: 0.6730 Epoch 79/1000, Train Loss: 0.6368, Train Accuracy: 0.6746, Val Loss: 0.6369, Val Accuracy: 0.6797 Epoch 80/1000, Train Loss: 0.6363, Train Accuracy: 0.6743, Val Loss: 0.6356, Val Accuracy: 0.6850 Epoch 81/1000, Train Loss: 0.6343, Train Accuracy: 0.6799, Val Loss: 0.6343, Val Accuracy: 0.6919 Epoch 82/1000, Train Loss: 0.6336, Train Accuracy: 0.6825, Val Loss: 0.6330, Val Accuracy: 0.6971 Epoch 83/1000, Train Loss: 0.6328, Train Accuracy: 0.6826, Val Loss: 0.6318, Val Accuracy: 0.7034 Epoch 84/1000, Train Loss: 0.6325, Train Accuracy: 0.6817, Val Loss: 0.6305, Val Accuracy: 0.7107 Epoch 85/1000, Train Loss: 0.6309, Train Accuracy: 0.6899, Val Loss: 0.6292, Val Accuracy: 0.7169 Epoch 86/1000, Train Loss: 0.6292, Train Accuracy: 0.6936, Val Loss: 0.6279, Val Accuracy: 0.7219 Epoch 87/1000, Train Loss: 0.6284, Train Accuracy: 0.6959, Val Loss: 0.6267, Val Accuracy: 0.7255 Epoch 88/1000, Train Loss: 0.6280, Train Accuracy: 0.6955, Val Loss: 0.6254, Val Accuracy: 0.7296 Epoch 89/1000, Train Loss: 0.6260, Train Accuracy: 0.7027, Val Loss: 0.6241, Val Accuracy: 0.7330 Epoch 90/1000, Train Loss: 0.6253, Train Accuracy: 0.7022, Val Loss: 0.6229, Val Accuracy: 0.7356 Epoch 91/1000, Train Loss: 0.6237, Train Accuracy: 0.7081, Val Loss: 0.6216, Val Accuracy: 0.7386 Epoch 92/1000, Train Loss: 0.6229, Train Accuracy: 0.7118, Val Loss: 0.6203, Val Accuracy: 0.7415 Epoch 93/1000, Train Loss: 0.6216, Train Accuracy: 0.7158, Val Loss: 0.6191, Val Accuracy: 0.7437 Epoch 94/1000, Train Loss: 0.6201, Train Accuracy: 0.7200, Val Loss: 0.6178, Val Accuracy: 0.7480 Epoch 95/1000, Train Loss: 0.6188, Train Accuracy: 0.7218, Val Loss: 0.6165, Val Accuracy: 0.7520 Epoch 96/1000, Train Loss: 0.6185, Train Accuracy: 0.7211, Val Loss: 0.6152, Val Accuracy: 0.7556 Epoch 97/1000, Train Loss: 0.6171, Train Accuracy: 0.7221, Val Loss: 0.6139, Val Accuracy: 0.7587 Epoch 98/1000, Train Loss: 0.6156, Train Accuracy: 0.7271, Val Loss: 0.6126, Val Accuracy: 0.7616 Epoch 99/1000, Train Loss: 0.6143, Train Accuracy: 0.7317, Val Loss: 0.6113, Val Accuracy: 0.7645 Epoch 100/1000, Train Loss: 0.6132, Train Accuracy: 0.7306, Val Loss: 0.6100, Val Accuracy: 0.7678 Epoch 101/1000, Train Loss: 0.6118, Train Accuracy: 0.7375, Val Loss: 0.6086, Val Accuracy: 0.7713 Epoch 102/1000, Train Loss: 0.6102, Train Accuracy: 0.7421, Val Loss: 0.6073, Val Accuracy: 0.7744 Epoch 103/1000, Train Loss: 0.6096, Train Accuracy: 0.7413, Val Loss: 0.6059, Val Accuracy: 0.7776 Epoch 104/1000, Train Loss: 0.6077, Train Accuracy: 0.7445, Val Loss: 0.6046, Val Accuracy: 0.7812 Epoch 105/1000, Train Loss: 0.6071, Train Accuracy: 0.7474, Val Loss: 0.6032, Val Accuracy: 0.7843 Epoch 106/1000, Train Loss: 0.6047, Train Accuracy: 0.7530, Val Loss: 0.6018, Val Accuracy: 0.7871 Epoch 107/1000, Train Loss: 0.6034, Train Accuracy: 0.7526, Val Loss: 0.6004, Val Accuracy: 0.7898 Epoch 108/1000, Train Loss: 0.6025, Train Accuracy: 0.7588, Val Loss: 0.5990, Val Accuracy: 0.7924 Epoch 109/1000, Train Loss: 0.6018, Train Accuracy: 0.7586, Val Loss: 0.5975, Val Accuracy: 0.7943 Epoch 110/1000, Train Loss: 0.6003, Train Accuracy: 0.7621, Val Loss: 0.5961, Val Accuracy: 0.7959 Epoch 111/1000, Train Loss: 0.5982, Train Accuracy: 0.7650, Val Loss: 0.5947, Val Accuracy: 0.8005 Epoch 112/1000, Train Loss: 0.5971, Train Accuracy: 0.7624, Val Loss: 0.5932, Val Accuracy: 0.8040 Epoch 113/1000, Train Loss: 0.5958, Train Accuracy: 0.7627, Val Loss: 0.5917, Val Accuracy: 0.8046 Epoch 114/1000, Train Loss: 0.5944, Train Accuracy: 0.7663, Val Loss: 0.5902, Val Accuracy: 0.8067 Epoch 115/1000, Train Loss: 0.5939, Train Accuracy: 0.7692, Val Loss: 0.5887, Val Accuracy: 0.8091 Epoch 116/1000, Train Loss: 0.5915, Train Accuracy: 0.7734, Val Loss: 0.5872, Val Accuracy: 0.8112 Epoch 117/1000, Train Loss: 0.5896, Train Accuracy: 0.7752, Val Loss: 0.5857, Val Accuracy: 0.8131 Epoch 118/1000, Train Loss: 0.5881, Train Accuracy: 0.7778, Val Loss: 0.5841, Val Accuracy: 0.8157 Epoch 119/1000, Train Loss: 0.5873, Train Accuracy: 0.7798, Val Loss: 0.5825, Val Accuracy: 0.8165 Epoch 120/1000, Train Loss: 0.5850, Train Accuracy: 0.7831, Val Loss: 0.5810, Val Accuracy: 0.8172 Epoch 121/1000, Train Loss: 0.5829, Train Accuracy: 0.7849, Val Loss: 0.5794, Val Accuracy: 0.8196 Epoch 122/1000, Train Loss: 0.5823, Train Accuracy: 0.7843, Val Loss: 0.5778, Val Accuracy: 0.8215 Epoch 123/1000, Train Loss: 0.5802, Train Accuracy: 0.7890, Val Loss: 0.5762, Val Accuracy: 0.8231 Epoch 124/1000, Train Loss: 0.5792, Train Accuracy: 0.7876, Val Loss: 0.5745, Val Accuracy: 0.8250 Epoch 125/1000, Train Loss: 0.5776, Train Accuracy: 0.7910, Val Loss: 0.5728, Val Accuracy: 0.8263 Epoch 126/1000, Train Loss: 0.5752, Train Accuracy: 0.7940, Val Loss: 0.5711, Val Accuracy: 0.8270 Epoch 127/1000, Train Loss: 0.5739, Train Accuracy: 0.7913, Val Loss: 0.5694, Val Accuracy: 0.8284 Epoch 128/1000, Train Loss: 0.5726, Train Accuracy: 0.7973, Val Loss: 0.5678, Val Accuracy: 0.8301 Epoch 129/1000, Train Loss: 0.5709, Train Accuracy: 0.7981, Val Loss: 0.5660, Val Accuracy: 0.8320 Epoch 130/1000, Train Loss: 0.5688, Train Accuracy: 0.8012, Val Loss: 0.5643, Val Accuracy: 0.8336 Epoch 131/1000, Train Loss: 0.5666, Train Accuracy: 0.8024, Val Loss: 0.5625, Val Accuracy: 0.8349 Epoch 132/1000, Train Loss: 0.5659, Train Accuracy: 0.8016, Val Loss: 0.5607, Val Accuracy: 0.8358 Epoch 133/1000, Train Loss: 0.5640, Train Accuracy: 0.8054, Val Loss: 0.5590, Val Accuracy: 0.8367 Epoch 134/1000, Train Loss: 0.5619, Train Accuracy: 0.8050, Val Loss: 0.5572, Val Accuracy: 0.8367 Epoch 135/1000, Train Loss: 0.5600, Train Accuracy: 0.8091, Val Loss: 0.5553, Val Accuracy: 0.8370 Epoch 136/1000, Train Loss: 0.5591, Train Accuracy: 0.8083, Val Loss: 0.5535, Val Accuracy: 0.8367 Epoch 137/1000, Train Loss: 0.5569, Train Accuracy: 0.8119, Val Loss: 0.5517, Val Accuracy: 0.8377 Epoch 138/1000, Train Loss: 0.5551, Train Accuracy: 0.8098, Val Loss: 0.5498, Val Accuracy: 0.8396 Epoch 139/1000, Train Loss: 0.5534, Train Accuracy: 0.8152, Val Loss: 0.5479, Val Accuracy: 0.8398 Epoch 140/1000, Train Loss: 0.5513, Train Accuracy: 0.8174, Val Loss: 0.5460, Val Accuracy: 0.8415 Epoch 141/1000, Train Loss: 0.5487, Train Accuracy: 0.8168, Val Loss: 0.5441, Val Accuracy: 0.8432 Epoch 142/1000, Train Loss: 0.5475, Train Accuracy: 0.8151, Val Loss: 0.5421, Val Accuracy: 0.8446 Epoch 143/1000, Train Loss: 0.5453, Train Accuracy: 0.8170, Val Loss: 0.5401, Val Accuracy: 0.8460 Epoch 144/1000, Train Loss: 0.5433, Train Accuracy: 0.8191, Val Loss: 0.5381, Val Accuracy: 0.8470 Epoch 145/1000, Train Loss: 0.5414, Train Accuracy: 0.8215, Val Loss: 0.5361, Val Accuracy: 0.8473 Epoch 146/1000, Train Loss: 0.5400, Train Accuracy: 0.8255, Val Loss: 0.5342, Val Accuracy: 0.8485 Epoch 147/1000, Train Loss: 0.5380, Train Accuracy: 0.8219, Val Loss: 0.5321, Val Accuracy: 0.8499 Epoch 148/1000, Train Loss: 0.5351, Train Accuracy: 0.8265, Val Loss: 0.5301, Val Accuracy: 0.8496 Epoch 149/1000, Train Loss: 0.5330, Train Accuracy: 0.8265, Val Loss: 0.5280, Val Accuracy: 0.8506 Epoch 150/1000, Train Loss: 0.5307, Train Accuracy: 0.8291, Val Loss: 0.5260, Val Accuracy: 0.8518 Epoch 151/1000, Train Loss: 0.5288, Train Accuracy: 0.8300, Val Loss: 0.5238, Val Accuracy: 0.8518 Epoch 152/1000, Train Loss: 0.5271, Train Accuracy: 0.8286, Val Loss: 0.5217, Val Accuracy: 0.8528 Epoch 153/1000, Train Loss: 0.5248, Train Accuracy: 0.8331, Val Loss: 0.5196, Val Accuracy: 0.8534 Epoch 154/1000, Train Loss: 0.5230, Train Accuracy: 0.8325, Val Loss: 0.5175, Val Accuracy: 0.8540 Epoch 155/1000, Train Loss: 0.5213, Train Accuracy: 0.8325, Val Loss: 0.5153, Val Accuracy: 0.8551 Epoch 156/1000, Train Loss: 0.5188, Train Accuracy: 0.8347, Val Loss: 0.5132, Val Accuracy: 0.8563 Epoch 157/1000, Train Loss: 0.5160, Train Accuracy: 0.8383, Val Loss: 0.5110, Val Accuracy: 0.8578 Epoch 158/1000, Train Loss: 0.5151, Train Accuracy: 0.8369, Val Loss: 0.5088, Val Accuracy: 0.8585 Epoch 159/1000, Train Loss: 0.5135, Train Accuracy: 0.8382, Val Loss: 0.5067, Val Accuracy: 0.8599 Epoch 160/1000, Train Loss: 0.5105, Train Accuracy: 0.8408, Val Loss: 0.5045, Val Accuracy: 0.8614 Epoch 161/1000, Train Loss: 0.5092, Train Accuracy: 0.8395, Val Loss: 0.5023, Val Accuracy: 0.8627 Epoch 162/1000, Train Loss: 0.5057, Train Accuracy: 0.8452, Val Loss: 0.5001, Val Accuracy: 0.8644 Epoch 163/1000, Train Loss: 0.5045, Train Accuracy: 0.8430, Val Loss: 0.4978, Val Accuracy: 0.8661 Epoch 164/1000, Train Loss: 0.5019, Train Accuracy: 0.8442, Val Loss: 0.4955, Val Accuracy: 0.8666 Epoch 165/1000, Train Loss: 0.4994, Train Accuracy: 0.8465, Val Loss: 0.4932, Val Accuracy: 0.8670 Epoch 166/1000, Train Loss: 0.4970, Train Accuracy: 0.8461, Val Loss: 0.4909, Val Accuracy: 0.8670 Epoch 167/1000, Train Loss: 0.4954, Train Accuracy: 0.8454, Val Loss: 0.4886, Val Accuracy: 0.8675 Epoch 168/1000, Train Loss: 0.4927, Train Accuracy: 0.8474, Val Loss: 0.4862, Val Accuracy: 0.8695 Epoch 169/1000, Train Loss: 0.4910, Train Accuracy: 0.8492, Val Loss: 0.4839, Val Accuracy: 0.8707 Epoch 170/1000, Train Loss: 0.4881, Train Accuracy: 0.8481, Val Loss: 0.4815, Val Accuracy: 0.8718 Epoch 171/1000, Train Loss: 0.4862, Train Accuracy: 0.8513, Val Loss: 0.4792, Val Accuracy: 0.8721 Epoch 172/1000, Train Loss: 0.4848, Train Accuracy: 0.8491, Val Loss: 0.4769, Val Accuracy: 0.8728 Epoch 173/1000, Train Loss: 0.4813, Train Accuracy: 0.8542, Val Loss: 0.4745, Val Accuracy: 0.8749 Epoch 174/1000, Train Loss: 0.4788, Train Accuracy: 0.8553, Val Loss: 0.4722, Val Accuracy: 0.8752 Epoch 175/1000, Train Loss: 0.4763, Train Accuracy: 0.8525, Val Loss: 0.4698, Val Accuracy: 0.8756 Epoch 176/1000, Train Loss: 0.4743, Train Accuracy: 0.8554, Val Loss: 0.4674, Val Accuracy: 0.8761 Epoch 177/1000, Train Loss: 0.4716, Train Accuracy: 0.8586, Val Loss: 0.4650, Val Accuracy: 0.8778 Epoch 178/1000, Train Loss: 0.4694, Train Accuracy: 0.8573, Val Loss: 0.4626, Val Accuracy: 0.8788 Epoch 179/1000, Train Loss: 0.4673, Train Accuracy: 0.8577, Val Loss: 0.4602, Val Accuracy: 0.8797 Epoch 180/1000, Train Loss: 0.4663, Train Accuracy: 0.8545, Val Loss: 0.4578, Val Accuracy: 0.8809 Epoch 181/1000, Train Loss: 0.4622, Train Accuracy: 0.8589, Val Loss: 0.4554, Val Accuracy: 0.8821 Epoch 182/1000, Train Loss: 0.4612, Train Accuracy: 0.8590, Val Loss: 0.4530, Val Accuracy: 0.8828 Epoch 183/1000, Train Loss: 0.4587, Train Accuracy: 0.8607, Val Loss: 0.4505, Val Accuracy: 0.8833 Epoch 184/1000, Train Loss: 0.4551, Train Accuracy: 0.8637, Val Loss: 0.4481, Val Accuracy: 0.8842 Epoch 185/1000, Train Loss: 0.4532, Train Accuracy: 0.8632, Val Loss: 0.4457, Val Accuracy: 0.8845 Epoch 186/1000, Train Loss: 0.4517, Train Accuracy: 0.8626, Val Loss: 0.4433, Val Accuracy: 0.8847 Epoch 187/1000, Train Loss: 0.4499, Train Accuracy: 0.8632, Val Loss: 0.4408, Val Accuracy: 0.8855 Epoch 188/1000, Train Loss: 0.4470, Train Accuracy: 0.8659, Val Loss: 0.4384, Val Accuracy: 0.8871 Epoch 189/1000, Train Loss: 0.4426, Train Accuracy: 0.8710, Val Loss: 0.4359, Val Accuracy: 0.8878 Epoch 190/1000, Train Loss: 0.4419, Train Accuracy: 0.8661, Val Loss: 0.4335, Val Accuracy: 0.8880 Epoch 191/1000, Train Loss: 0.4394, Train Accuracy: 0.8688, Val Loss: 0.4311, Val Accuracy: 0.8886 Epoch 192/1000, Train Loss: 0.4374, Train Accuracy: 0.8670, Val Loss: 0.4286, Val Accuracy: 0.8892 Epoch 193/1000, Train Loss: 0.4343, Train Accuracy: 0.8684, Val Loss: 0.4262, Val Accuracy: 0.8900 Epoch 194/1000, Train Loss: 0.4311, Train Accuracy: 0.8703, Val Loss: 0.4237, Val Accuracy: 0.8904 Epoch 195/1000, Train Loss: 0.4302, Train Accuracy: 0.8722, Val Loss: 0.4213, Val Accuracy: 0.8909 Epoch 196/1000, Train Loss: 0.4266, Train Accuracy: 0.8737, Val Loss: 0.4189, Val Accuracy: 0.8914 Epoch 197/1000, Train Loss: 0.4250, Train Accuracy: 0.8727, Val Loss: 0.4164, Val Accuracy: 0.8912 Epoch 198/1000, Train Loss: 0.4230, Train Accuracy: 0.8741, Val Loss: 0.4140, Val Accuracy: 0.8921 Epoch 199/1000, Train Loss: 0.4211, Train Accuracy: 0.8751, Val Loss: 0.4116, Val Accuracy: 0.8928 Epoch 200/1000, Train Loss: 0.4175, Train Accuracy: 0.8774, Val Loss: 0.4092, Val Accuracy: 0.8931 Epoch 201/1000, Train Loss: 0.4160, Train Accuracy: 0.8747, Val Loss: 0.4068, Val Accuracy: 0.8929 Epoch 202/1000, Train Loss: 0.4137, Train Accuracy: 0.8770, Val Loss: 0.4044, Val Accuracy: 0.8936 Epoch 203/1000, Train Loss: 0.4108, Train Accuracy: 0.8786, Val Loss: 0.4021, Val Accuracy: 0.8943 Epoch 204/1000, Train Loss: 0.4087, Train Accuracy: 0.8779, Val Loss: 0.3996, Val Accuracy: 0.8950 Epoch 205/1000, Train Loss: 0.4068, Train Accuracy: 0.8800, Val Loss: 0.3972, Val Accuracy: 0.8954 Epoch 206/1000, Train Loss: 0.4045, Train Accuracy: 0.8813, Val Loss: 0.3949, Val Accuracy: 0.8957 Epoch 207/1000, Train Loss: 0.4012, Train Accuracy: 0.8801, Val Loss: 0.3925, Val Accuracy: 0.8955 Epoch 208/1000, Train Loss: 0.4003, Train Accuracy: 0.8806, Val Loss: 0.3901, Val Accuracy: 0.8962 Epoch 209/1000, Train Loss: 0.3969, Train Accuracy: 0.8841, Val Loss: 0.3877, Val Accuracy: 0.8971 Epoch 210/1000, Train Loss: 0.3959, Train Accuracy: 0.8820, Val Loss: 0.3854, Val Accuracy: 0.8976 Epoch 211/1000, Train Loss: 0.3925, Train Accuracy: 0.8840, Val Loss: 0.3831, Val Accuracy: 0.8983 Epoch 212/1000, Train Loss: 0.3915, Train Accuracy: 0.8814, Val Loss: 0.3807, Val Accuracy: 0.8988 Epoch 213/1000, Train Loss: 0.3886, Train Accuracy: 0.8831, Val Loss: 0.3784, Val Accuracy: 0.8991 Epoch 214/1000, Train Loss: 0.3861, Train Accuracy: 0.8866, Val Loss: 0.3760, Val Accuracy: 0.8998 Epoch 215/1000, Train Loss: 0.3838, Train Accuracy: 0.8877, Val Loss: 0.3738, Val Accuracy: 0.9003 Epoch 216/1000, Train Loss: 0.3814, Train Accuracy: 0.8889, Val Loss: 0.3714, Val Accuracy: 0.9007 Epoch 217/1000, Train Loss: 0.3784, Train Accuracy: 0.8866, Val Loss: 0.3691, Val Accuracy: 0.9014 Epoch 218/1000, Train Loss: 0.3787, Train Accuracy: 0.8875, Val Loss: 0.3668, Val Accuracy: 0.9019 Epoch 219/1000, Train Loss: 0.3749, Train Accuracy: 0.8884, Val Loss: 0.3645, Val Accuracy: 0.9028 Epoch 220/1000, Train Loss: 0.3728, Train Accuracy: 0.8912, Val Loss: 0.3622, Val Accuracy: 0.9034 Epoch 221/1000, Train Loss: 0.3706, Train Accuracy: 0.8901, Val Loss: 0.3600, Val Accuracy: 0.9038 Epoch 222/1000, Train Loss: 0.3680, Train Accuracy: 0.8898, Val Loss: 0.3578, Val Accuracy: 0.9045 Epoch 223/1000, Train Loss: 0.3649, Train Accuracy: 0.8946, Val Loss: 0.3555, Val Accuracy: 0.9048 Epoch 224/1000, Train Loss: 0.3636, Train Accuracy: 0.8905, Val Loss: 0.3533, Val Accuracy: 0.9055 Epoch 225/1000, Train Loss: 0.3618, Train Accuracy: 0.8923, Val Loss: 0.3511, Val Accuracy: 0.9062 Epoch 226/1000, Train Loss: 0.3608, Train Accuracy: 0.8922, Val Loss: 0.3489, Val Accuracy: 0.9062 Epoch 227/1000, Train Loss: 0.3580, Train Accuracy: 0.8919, Val Loss: 0.3467, Val Accuracy: 0.9064 Epoch 228/1000, Train Loss: 0.3544, Train Accuracy: 0.8958, Val Loss: 0.3445, Val Accuracy: 0.9067 Epoch 229/1000, Train Loss: 0.3541, Train Accuracy: 0.8942, Val Loss: 0.3424, Val Accuracy: 0.9072 Epoch 230/1000, Train Loss: 0.3506, Train Accuracy: 0.8943, Val Loss: 0.3402, Val Accuracy: 0.9081 Epoch 231/1000, Train Loss: 0.3475, Train Accuracy: 0.8962, Val Loss: 0.3381, Val Accuracy: 0.9084 Epoch 232/1000, Train Loss: 0.3463, Train Accuracy: 0.8976, Val Loss: 0.3360, Val Accuracy: 0.9083 Epoch 233/1000, Train Loss: 0.3429, Train Accuracy: 0.8983, Val Loss: 0.3339, Val Accuracy: 0.9079 Epoch 234/1000, Train Loss: 0.3419, Train Accuracy: 0.8991, Val Loss: 0.3317, Val Accuracy: 0.9081 Epoch 235/1000, Train Loss: 0.3403, Train Accuracy: 0.8966, Val Loss: 0.3296, Val Accuracy: 0.9084 Epoch 236/1000, Train Loss: 0.3401, Train Accuracy: 0.8958, Val Loss: 0.3275, Val Accuracy: 0.9086 Epoch 237/1000, Train Loss: 0.3370, Train Accuracy: 0.8981, Val Loss: 0.3255, Val Accuracy: 0.9090 Epoch 238/1000, Train Loss: 0.3348, Train Accuracy: 0.8996, Val Loss: 0.3235, Val Accuracy: 0.9096 Epoch 239/1000, Train Loss: 0.3326, Train Accuracy: 0.9003, Val Loss: 0.3215, Val Accuracy: 0.9105 Epoch 240/1000, Train Loss: 0.3301, Train Accuracy: 0.8999, Val Loss: 0.3195, Val Accuracy: 0.9105 Epoch 241/1000, Train Loss: 0.3290, Train Accuracy: 0.9025, Val Loss: 0.3175, Val Accuracy: 0.9107 Epoch 242/1000, Train Loss: 0.3257, Train Accuracy: 0.9009, Val Loss: 0.3155, Val Accuracy: 0.9112 Epoch 243/1000, Train Loss: 0.3247, Train Accuracy: 0.9026, Val Loss: 0.3136, Val Accuracy: 0.9114 Epoch 244/1000, Train Loss: 0.3229, Train Accuracy: 0.9022, Val Loss: 0.3116, Val Accuracy: 0.9120 Epoch 245/1000, Train Loss: 0.3199, Train Accuracy: 0.9033, Val Loss: 0.3097, Val Accuracy: 0.9120 Epoch 246/1000, Train Loss: 0.3185, Train Accuracy: 0.9057, Val Loss: 0.3078, Val Accuracy: 0.9131 Epoch 247/1000, Train Loss: 0.3164, Train Accuracy: 0.9053, Val Loss: 0.3058, Val Accuracy: 0.9131 Epoch 248/1000, Train Loss: 0.3160, Train Accuracy: 0.9054, Val Loss: 0.3040, Val Accuracy: 0.9133 Epoch 249/1000, Train Loss: 0.3139, Train Accuracy: 0.9055, Val Loss: 0.3021, Val Accuracy: 0.9138 Epoch 250/1000, Train Loss: 0.3125, Train Accuracy: 0.9042, Val Loss: 0.3003, Val Accuracy: 0.9141 Epoch 251/1000, Train Loss: 0.3089, Train Accuracy: 0.9057, Val Loss: 0.2985, Val Accuracy: 0.9143 Epoch 252/1000, Train Loss: 0.3072, Train Accuracy: 0.9069, Val Loss: 0.2967, Val Accuracy: 0.9146 Epoch 253/1000, Train Loss: 0.3076, Train Accuracy: 0.9065, Val Loss: 0.2949, Val Accuracy: 0.9148 Epoch 254/1000, Train Loss: 0.3046, Train Accuracy: 0.9072, Val Loss: 0.2931, Val Accuracy: 0.9150 Epoch 255/1000, Train Loss: 0.3030, Train Accuracy: 0.9078, Val Loss: 0.2915, Val Accuracy: 0.9151 Epoch 256/1000, Train Loss: 0.3011, Train Accuracy: 0.9083, Val Loss: 0.2897, Val Accuracy: 0.9155 Epoch 257/1000, Train Loss: 0.2993, Train Accuracy: 0.9083, Val Loss: 0.2880, Val Accuracy: 0.9153 Epoch 258/1000, Train Loss: 0.2980, Train Accuracy: 0.9081, Val Loss: 0.2863, Val Accuracy: 0.9155 Epoch 259/1000, Train Loss: 0.2964, Train Accuracy: 0.9100, Val Loss: 0.2846, Val Accuracy: 0.9158 Epoch 260/1000, Train Loss: 0.2944, Train Accuracy: 0.9092, Val Loss: 0.2830, Val Accuracy: 0.9164 Epoch 261/1000, Train Loss: 0.2936, Train Accuracy: 0.9111, Val Loss: 0.2813, Val Accuracy: 0.9172 Epoch 262/1000, Train Loss: 0.2905, Train Accuracy: 0.9100, Val Loss: 0.2797, Val Accuracy: 0.9172 Epoch 263/1000, Train Loss: 0.2877, Train Accuracy: 0.9112, Val Loss: 0.2781, Val Accuracy: 0.9176 Epoch 264/1000, Train Loss: 0.2884, Train Accuracy: 0.9110, Val Loss: 0.2765, Val Accuracy: 0.9184 Epoch 265/1000, Train Loss: 0.2861, Train Accuracy: 0.9107, Val Loss: 0.2749, Val Accuracy: 0.9184 Epoch 266/1000, Train Loss: 0.2835, Train Accuracy: 0.9145, Val Loss: 0.2734, Val Accuracy: 0.9191 Epoch 267/1000, Train Loss: 0.2831, Train Accuracy: 0.9124, Val Loss: 0.2718, Val Accuracy: 0.9196 Epoch 268/1000, Train Loss: 0.2803, Train Accuracy: 0.9138, Val Loss: 0.2703, Val Accuracy: 0.9196 Epoch 269/1000, Train Loss: 0.2819, Train Accuracy: 0.9126, Val Loss: 0.2687, Val Accuracy: 0.9201 Epoch 270/1000, Train Loss: 0.2786, Train Accuracy: 0.9135, Val Loss: 0.2673, Val Accuracy: 0.9210 Epoch 271/1000, Train Loss: 0.2772, Train Accuracy: 0.9143, Val Loss: 0.2658, Val Accuracy: 0.9212 Epoch 272/1000, Train Loss: 0.2760, Train Accuracy: 0.9126, Val Loss: 0.2644, Val Accuracy: 0.9212 Epoch 273/1000, Train Loss: 0.2737, Train Accuracy: 0.9149, Val Loss: 0.2629, Val Accuracy: 0.9213 Epoch 274/1000, Train Loss: 0.2729, Train Accuracy: 0.9145, Val Loss: 0.2615, Val Accuracy: 0.9217 Epoch 275/1000, Train Loss: 0.2709, Train Accuracy: 0.9151, Val Loss: 0.2600, Val Accuracy: 0.9217 Epoch 276/1000, Train Loss: 0.2700, Train Accuracy: 0.9156, Val Loss: 0.2586, Val Accuracy: 0.9217 Epoch 277/1000, Train Loss: 0.2692, Train Accuracy: 0.9166, Val Loss: 0.2572, Val Accuracy: 0.9222 Epoch 278/1000, Train Loss: 0.2682, Train Accuracy: 0.9139, Val Loss: 0.2559, Val Accuracy: 0.9222 Epoch 279/1000, Train Loss: 0.2661, Train Accuracy: 0.9172, Val Loss: 0.2545, Val Accuracy: 0.9222 Epoch 280/1000, Train Loss: 0.2649, Train Accuracy: 0.9159, Val Loss: 0.2532, Val Accuracy: 0.9225 Epoch 281/1000, Train Loss: 0.2648, Train Accuracy: 0.9161, Val Loss: 0.2519, Val Accuracy: 0.9234 Epoch 282/1000, Train Loss: 0.2628, Train Accuracy: 0.9156, Val Loss: 0.2507, Val Accuracy: 0.9238 Epoch 283/1000, Train Loss: 0.2605, Train Accuracy: 0.9171, Val Loss: 0.2494, Val Accuracy: 0.9241 Epoch 284/1000, Train Loss: 0.2593, Train Accuracy: 0.9169, Val Loss: 0.2481, Val Accuracy: 0.9241 Epoch 285/1000, Train Loss: 0.2584, Train Accuracy: 0.9181, Val Loss: 0.2468, Val Accuracy: 0.9241 Epoch 286/1000, Train Loss: 0.2564, Train Accuracy: 0.9189, Val Loss: 0.2455, Val Accuracy: 0.9246 Epoch 287/1000, Train Loss: 0.2572, Train Accuracy: 0.9166, Val Loss: 0.2443, Val Accuracy: 0.9251 Epoch 288/1000, Train Loss: 0.2530, Train Accuracy: 0.9206, Val Loss: 0.2431, Val Accuracy: 0.9250 Epoch 289/1000, Train Loss: 0.2523, Train Accuracy: 0.9193, Val Loss: 0.2418, Val Accuracy: 0.9251 Epoch 290/1000, Train Loss: 0.2516, Train Accuracy: 0.9206, Val Loss: 0.2406, Val Accuracy: 0.9251 Epoch 291/1000, Train Loss: 0.2492, Train Accuracy: 0.9187, Val Loss: 0.2394, Val Accuracy: 0.9256 Epoch 292/1000, Train Loss: 0.2496, Train Accuracy: 0.9214, Val Loss: 0.2383, Val Accuracy: 0.9260 Epoch 293/1000, Train Loss: 0.2482, Train Accuracy: 0.9191, Val Loss: 0.2372, Val Accuracy: 0.9262 Epoch 294/1000, Train Loss: 0.2480, Train Accuracy: 0.9187, Val Loss: 0.2361, Val Accuracy: 0.9262 Epoch 295/1000, Train Loss: 0.2448, Train Accuracy: 0.9224, Val Loss: 0.2349, Val Accuracy: 0.9260 Epoch 296/1000, Train Loss: 0.2447, Train Accuracy: 0.9212, Val Loss: 0.2338, Val Accuracy: 0.9265 Epoch 297/1000, Train Loss: 0.2443, Train Accuracy: 0.9209, Val Loss: 0.2327, Val Accuracy: 0.9267 Epoch 298/1000, Train Loss: 0.2448, Train Accuracy: 0.9210, Val Loss: 0.2316, Val Accuracy: 0.9267 Epoch 299/1000, Train Loss: 0.2406, Train Accuracy: 0.9208, Val Loss: 0.2304, Val Accuracy: 0.9269 Epoch 300/1000, Train Loss: 0.2385, Train Accuracy: 0.9237, Val Loss: 0.2294, Val Accuracy: 0.9275 Epoch 301/1000, Train Loss: 0.2379, Train Accuracy: 0.9226, Val Loss: 0.2283, Val Accuracy: 0.9277 Epoch 302/1000, Train Loss: 0.2387, Train Accuracy: 0.9201, Val Loss: 0.2273, Val Accuracy: 0.9277 Epoch 303/1000, Train Loss: 0.2375, Train Accuracy: 0.9237, Val Loss: 0.2262, Val Accuracy: 0.9284 Epoch 304/1000, Train Loss: 0.2353, Train Accuracy: 0.9236, Val Loss: 0.2252, Val Accuracy: 0.9281 Epoch 305/1000, Train Loss: 0.2359, Train Accuracy: 0.9223, Val Loss: 0.2243, Val Accuracy: 0.9279 Epoch 306/1000, Train Loss: 0.2338, Train Accuracy: 0.9237, Val Loss: 0.2233, Val Accuracy: 0.9284 Epoch 307/1000, Train Loss: 0.2316, Train Accuracy: 0.9250, Val Loss: 0.2222, Val Accuracy: 0.9282 Epoch 308/1000, Train Loss: 0.2317, Train Accuracy: 0.9249, Val Loss: 0.2212, Val Accuracy: 0.9282 Epoch 309/1000, Train Loss: 0.2318, Train Accuracy: 0.9244, Val Loss: 0.2203, Val Accuracy: 0.9279 Epoch 310/1000, Train Loss: 0.2304, Train Accuracy: 0.9237, Val Loss: 0.2194, Val Accuracy: 0.9284 Epoch 311/1000, Train Loss: 0.2291, Train Accuracy: 0.9240, Val Loss: 0.2184, Val Accuracy: 0.9289 Epoch 312/1000, Train Loss: 0.2275, Train Accuracy: 0.9249, Val Loss: 0.2175, Val Accuracy: 0.9293 Epoch 313/1000, Train Loss: 0.2271, Train Accuracy: 0.9264, Val Loss: 0.2166, Val Accuracy: 0.9293 Epoch 314/1000, Train Loss: 0.2268, Train Accuracy: 0.9237, Val Loss: 0.2157, Val Accuracy: 0.9298 Epoch 315/1000, Train Loss: 0.2243, Train Accuracy: 0.9266, Val Loss: 0.2148, Val Accuracy: 0.9298 Epoch 316/1000, Train Loss: 0.2236, Train Accuracy: 0.9265, Val Loss: 0.2139, Val Accuracy: 0.9299 Epoch 317/1000, Train Loss: 0.2223, Train Accuracy: 0.9277, Val Loss: 0.2129, Val Accuracy: 0.9301 Epoch 318/1000, Train Loss: 0.2229, Train Accuracy: 0.9266, Val Loss: 0.2120, Val Accuracy: 0.9301 Epoch 319/1000, Train Loss: 0.2207, Train Accuracy: 0.9284, Val Loss: 0.2111, Val Accuracy: 0.9305 Epoch 320/1000, Train Loss: 0.2202, Train Accuracy: 0.9275, Val Loss: 0.2102, Val Accuracy: 0.9306 Epoch 321/1000, Train Loss: 0.2184, Train Accuracy: 0.9290, Val Loss: 0.2094, Val Accuracy: 0.9308 Epoch 322/1000, Train Loss: 0.2203, Train Accuracy: 0.9288, Val Loss: 0.2085, Val Accuracy: 0.9315 Epoch 323/1000, Train Loss: 0.2176, Train Accuracy: 0.9289, Val Loss: 0.2077, Val Accuracy: 0.9318 Epoch 324/1000, Train Loss: 0.2184, Train Accuracy: 0.9261, Val Loss: 0.2070, Val Accuracy: 0.9324 Epoch 325/1000, Train Loss: 0.2143, Train Accuracy: 0.9289, Val Loss: 0.2061, Val Accuracy: 0.9325 Epoch 326/1000, Train Loss: 0.2157, Train Accuracy: 0.9272, Val Loss: 0.2053, Val Accuracy: 0.9327 Epoch 327/1000, Train Loss: 0.2138, Train Accuracy: 0.9286, Val Loss: 0.2045, Val Accuracy: 0.9327 Epoch 328/1000, Train Loss: 0.2130, Train Accuracy: 0.9299, Val Loss: 0.2037, Val Accuracy: 0.9329 Epoch 329/1000, Train Loss: 0.2118, Train Accuracy: 0.9302, Val Loss: 0.2030, Val Accuracy: 0.9327 Epoch 330/1000, Train Loss: 0.2112, Train Accuracy: 0.9303, Val Loss: 0.2022, Val Accuracy: 0.9329 Epoch 331/1000, Train Loss: 0.2108, Train Accuracy: 0.9290, Val Loss: 0.2014, Val Accuracy: 0.9329 Epoch 332/1000, Train Loss: 0.2103, Train Accuracy: 0.9294, Val Loss: 0.2007, Val Accuracy: 0.9336 Epoch 333/1000, Train Loss: 0.2108, Train Accuracy: 0.9297, Val Loss: 0.1999, Val Accuracy: 0.9341 Epoch 334/1000, Train Loss: 0.2101, Train Accuracy: 0.9302, Val Loss: 0.1992, Val Accuracy: 0.9343 Epoch 335/1000, Train Loss: 0.2069, Train Accuracy: 0.9299, Val Loss: 0.1985, Val Accuracy: 0.9344 Epoch 336/1000, Train Loss: 0.2066, Train Accuracy: 0.9311, Val Loss: 0.1977, Val Accuracy: 0.9344 Epoch 337/1000, Train Loss: 0.2052, Train Accuracy: 0.9315, Val Loss: 0.1970, Val Accuracy: 0.9344 Epoch 338/1000, Train Loss: 0.2044, Train Accuracy: 0.9314, Val Loss: 0.1963, Val Accuracy: 0.9348 Epoch 339/1000, Train Loss: 0.2053, Train Accuracy: 0.9332, Val Loss: 0.1956, Val Accuracy: 0.9349 Epoch 340/1000, Train Loss: 0.2030, Train Accuracy: 0.9335, Val Loss: 0.1949, Val Accuracy: 0.9351 Epoch 341/1000, Train Loss: 0.2038, Train Accuracy: 0.9311, Val Loss: 0.1942, Val Accuracy: 0.9348 Epoch 342/1000, Train Loss: 0.2012, Train Accuracy: 0.9334, Val Loss: 0.1935, Val Accuracy: 0.9349 Epoch 343/1000, Train Loss: 0.2017, Train Accuracy: 0.9333, Val Loss: 0.1929, Val Accuracy: 0.9351 Epoch 344/1000, Train Loss: 0.2009, Train Accuracy: 0.9330, Val Loss: 0.1922, Val Accuracy: 0.9355 Epoch 345/1000, Train Loss: 0.2002, Train Accuracy: 0.9334, Val Loss: 0.1915, Val Accuracy: 0.9360 Epoch 346/1000, Train Loss: 0.2003, Train Accuracy: 0.9327, Val Loss: 0.1909, Val Accuracy: 0.9360 Epoch 347/1000, Train Loss: 0.1983, Train Accuracy: 0.9336, Val Loss: 0.1902, Val Accuracy: 0.9360 Epoch 348/1000, Train Loss: 0.1994, Train Accuracy: 0.9319, Val Loss: 0.1896, Val Accuracy: 0.9360 Epoch 349/1000, Train Loss: 0.1969, Train Accuracy: 0.9334, Val Loss: 0.1889, Val Accuracy: 0.9361 Epoch 350/1000, Train Loss: 0.1965, Train Accuracy: 0.9326, Val Loss: 0.1883, Val Accuracy: 0.9367 Epoch 351/1000, Train Loss: 0.1953, Train Accuracy: 0.9333, Val Loss: 0.1876, Val Accuracy: 0.9370 Epoch 352/1000, Train Loss: 0.1965, Train Accuracy: 0.9336, Val Loss: 0.1870, Val Accuracy: 0.9370 Epoch 353/1000, Train Loss: 0.1946, Train Accuracy: 0.9348, Val Loss: 0.1864, Val Accuracy: 0.9368 Epoch 354/1000, Train Loss: 0.1937, Train Accuracy: 0.9339, Val Loss: 0.1858, Val Accuracy: 0.9375 Epoch 355/1000, Train Loss: 0.1944, Train Accuracy: 0.9329, Val Loss: 0.1852, Val Accuracy: 0.9375 Epoch 356/1000, Train Loss: 0.1922, Train Accuracy: 0.9346, Val Loss: 0.1846, Val Accuracy: 0.9382 Epoch 357/1000, Train Loss: 0.1925, Train Accuracy: 0.9347, Val Loss: 0.1840, Val Accuracy: 0.9382 Epoch 358/1000, Train Loss: 0.1913, Train Accuracy: 0.9358, Val Loss: 0.1835, Val Accuracy: 0.9384 Epoch 359/1000, Train Loss: 0.1909, Train Accuracy: 0.9367, Val Loss: 0.1828, Val Accuracy: 0.9384 Epoch 360/1000, Train Loss: 0.1894, Train Accuracy: 0.9362, Val Loss: 0.1822, Val Accuracy: 0.9387 Epoch 361/1000, Train Loss: 0.1905, Train Accuracy: 0.9348, Val Loss: 0.1817, Val Accuracy: 0.9387 Epoch 362/1000, Train Loss: 0.1889, Train Accuracy: 0.9370, Val Loss: 0.1811, Val Accuracy: 0.9389 Epoch 363/1000, Train Loss: 0.1888, Train Accuracy: 0.9360, Val Loss: 0.1805, Val Accuracy: 0.9389 Epoch 364/1000, Train Loss: 0.1889, Train Accuracy: 0.9356, Val Loss: 0.1799, Val Accuracy: 0.9392 Epoch 365/1000, Train Loss: 0.1860, Train Accuracy: 0.9373, Val Loss: 0.1794, Val Accuracy: 0.9392 Epoch 366/1000, Train Loss: 0.1864, Train Accuracy: 0.9367, Val Loss: 0.1788, Val Accuracy: 0.9394 Epoch 367/1000, Train Loss: 0.1864, Train Accuracy: 0.9365, Val Loss: 0.1783, Val Accuracy: 0.9396 Epoch 368/1000, Train Loss: 0.1865, Train Accuracy: 0.9362, Val Loss: 0.1778, Val Accuracy: 0.9394 Epoch 369/1000, Train Loss: 0.1846, Train Accuracy: 0.9379, Val Loss: 0.1773, Val Accuracy: 0.9396 Epoch 370/1000, Train Loss: 0.1858, Train Accuracy: 0.9360, Val Loss: 0.1768, Val Accuracy: 0.9398 Epoch 371/1000, Train Loss: 0.1837, Train Accuracy: 0.9378, Val Loss: 0.1763, Val Accuracy: 0.9396 Epoch 372/1000, Train Loss: 0.1853, Train Accuracy: 0.9376, Val Loss: 0.1759, Val Accuracy: 0.9398 Epoch 373/1000, Train Loss: 0.1844, Train Accuracy: 0.9362, Val Loss: 0.1753, Val Accuracy: 0.9401 Epoch 374/1000, Train Loss: 0.1829, Train Accuracy: 0.9385, Val Loss: 0.1748, Val Accuracy: 0.9401 Epoch 375/1000, Train Loss: 0.1811, Train Accuracy: 0.9388, Val Loss: 0.1743, Val Accuracy: 0.9403 Epoch 376/1000, Train Loss: 0.1819, Train Accuracy: 0.9388, Val Loss: 0.1738, Val Accuracy: 0.9408 Epoch 377/1000, Train Loss: 0.1806, Train Accuracy: 0.9379, Val Loss: 0.1733, Val Accuracy: 0.9406 Epoch 378/1000, Train Loss: 0.1816, Train Accuracy: 0.9384, Val Loss: 0.1728, Val Accuracy: 0.9408 Epoch 379/1000, Train Loss: 0.1806, Train Accuracy: 0.9382, Val Loss: 0.1724, Val Accuracy: 0.9408 Epoch 380/1000, Train Loss: 0.1780, Train Accuracy: 0.9402, Val Loss: 0.1719, Val Accuracy: 0.9408 Epoch 381/1000, Train Loss: 0.1772, Train Accuracy: 0.9416, Val Loss: 0.1714, Val Accuracy: 0.9408 Epoch 382/1000, Train Loss: 0.1791, Train Accuracy: 0.9386, Val Loss: 0.1709, Val Accuracy: 0.9408 Epoch 383/1000, Train Loss: 0.1775, Train Accuracy: 0.9379, Val Loss: 0.1704, Val Accuracy: 0.9408 Epoch 384/1000, Train Loss: 0.1760, Train Accuracy: 0.9398, Val Loss: 0.1700, Val Accuracy: 0.9410 Epoch 385/1000, Train Loss: 0.1773, Train Accuracy: 0.9394, Val Loss: 0.1696, Val Accuracy: 0.9410 Epoch 386/1000, Train Loss: 0.1743, Train Accuracy: 0.9400, Val Loss: 0.1691, Val Accuracy: 0.9410 Epoch 387/1000, Train Loss: 0.1750, Train Accuracy: 0.9412, Val Loss: 0.1686, Val Accuracy: 0.9410 Epoch 388/1000, Train Loss: 0.1741, Train Accuracy: 0.9392, Val Loss: 0.1682, Val Accuracy: 0.9413 Epoch 389/1000, Train Loss: 0.1724, Train Accuracy: 0.9415, Val Loss: 0.1677, Val Accuracy: 0.9415 Epoch 390/1000, Train Loss: 0.1743, Train Accuracy: 0.9397, Val Loss: 0.1672, Val Accuracy: 0.9415 Epoch 391/1000, Train Loss: 0.1739, Train Accuracy: 0.9399, Val Loss: 0.1668, Val Accuracy: 0.9420 Epoch 392/1000, Train Loss: 0.1734, Train Accuracy: 0.9402, Val Loss: 0.1664, Val Accuracy: 0.9420 Epoch 393/1000, Train Loss: 0.1731, Train Accuracy: 0.9400, Val Loss: 0.1659, Val Accuracy: 0.9423 Epoch 394/1000, Train Loss: 0.1715, Train Accuracy: 0.9400, Val Loss: 0.1654, Val Accuracy: 0.9429 Epoch 395/1000, Train Loss: 0.1702, Train Accuracy: 0.9402, Val Loss: 0.1650, Val Accuracy: 0.9429 Epoch 396/1000, Train Loss: 0.1702, Train Accuracy: 0.9410, Val Loss: 0.1645, Val Accuracy: 0.9432 Epoch 397/1000, Train Loss: 0.1708, Train Accuracy: 0.9410, Val Loss: 0.1641, Val Accuracy: 0.9432 Epoch 398/1000, Train Loss: 0.1702, Train Accuracy: 0.9416, Val Loss: 0.1637, Val Accuracy: 0.9435 Epoch 399/1000, Train Loss: 0.1696, Train Accuracy: 0.9427, Val Loss: 0.1633, Val Accuracy: 0.9437 Epoch 400/1000, Train Loss: 0.1676, Train Accuracy: 0.9416, Val Loss: 0.1628, Val Accuracy: 0.9437 Epoch 401/1000, Train Loss: 0.1675, Train Accuracy: 0.9419, Val Loss: 0.1625, Val Accuracy: 0.9434 Epoch 402/1000, Train Loss: 0.1689, Train Accuracy: 0.9408, Val Loss: 0.1620, Val Accuracy: 0.9435 Epoch 403/1000, Train Loss: 0.1666, Train Accuracy: 0.9429, Val Loss: 0.1617, Val Accuracy: 0.9437 Epoch 404/1000, Train Loss: 0.1686, Train Accuracy: 0.9397, Val Loss: 0.1613, Val Accuracy: 0.9437 Epoch 405/1000, Train Loss: 0.1661, Train Accuracy: 0.9428, Val Loss: 0.1610, Val Accuracy: 0.9439 Epoch 406/1000, Train Loss: 0.1662, Train Accuracy: 0.9435, Val Loss: 0.1605, Val Accuracy: 0.9442 Epoch 407/1000, Train Loss: 0.1653, Train Accuracy: 0.9430, Val Loss: 0.1601, Val Accuracy: 0.9442 Epoch 408/1000, Train Loss: 0.1638, Train Accuracy: 0.9425, Val Loss: 0.1597, Val Accuracy: 0.9444 Epoch 409/1000, Train Loss: 0.1646, Train Accuracy: 0.9428, Val Loss: 0.1593, Val Accuracy: 0.9444 Epoch 410/1000, Train Loss: 0.1642, Train Accuracy: 0.9429, Val Loss: 0.1589, Val Accuracy: 0.9446 Epoch 411/1000, Train Loss: 0.1642, Train Accuracy: 0.9433, Val Loss: 0.1586, Val Accuracy: 0.9446 Epoch 412/1000, Train Loss: 0.1633, Train Accuracy: 0.9431, Val Loss: 0.1582, Val Accuracy: 0.9444 Epoch 413/1000, Train Loss: 0.1646, Train Accuracy: 0.9427, Val Loss: 0.1579, Val Accuracy: 0.9446 Epoch 414/1000, Train Loss: 0.1621, Train Accuracy: 0.9427, Val Loss: 0.1576, Val Accuracy: 0.9446 Epoch 415/1000, Train Loss: 0.1628, Train Accuracy: 0.9433, Val Loss: 0.1572, Val Accuracy: 0.9449 Epoch 416/1000, Train Loss: 0.1623, Train Accuracy: 0.9442, Val Loss: 0.1569, Val Accuracy: 0.9449 Epoch 417/1000, Train Loss: 0.1613, Train Accuracy: 0.9450, Val Loss: 0.1565, Val Accuracy: 0.9451 Epoch 418/1000, Train Loss: 0.1615, Train Accuracy: 0.9451, Val Loss: 0.1562, Val Accuracy: 0.9453 Epoch 419/1000, Train Loss: 0.1613, Train Accuracy: 0.9438, Val Loss: 0.1558, Val Accuracy: 0.9453 Epoch 420/1000, Train Loss: 0.1604, Train Accuracy: 0.9458, Val Loss: 0.1554, Val Accuracy: 0.9456 Epoch 421/1000, Train Loss: 0.1602, Train Accuracy: 0.9446, Val Loss: 0.1550, Val Accuracy: 0.9460 Epoch 422/1000, Train Loss: 0.1598, Train Accuracy: 0.9456, Val Loss: 0.1547, Val Accuracy: 0.9460 Epoch 423/1000, Train Loss: 0.1586, Train Accuracy: 0.9434, Val Loss: 0.1544, Val Accuracy: 0.9460 Epoch 424/1000, Train Loss: 0.1568, Train Accuracy: 0.9463, Val Loss: 0.1540, Val Accuracy: 0.9461 Epoch 425/1000, Train Loss: 0.1576, Train Accuracy: 0.9444, Val Loss: 0.1536, Val Accuracy: 0.9463 Epoch 426/1000, Train Loss: 0.1575, Train Accuracy: 0.9453, Val Loss: 0.1533, Val Accuracy: 0.9463 Epoch 427/1000, Train Loss: 0.1582, Train Accuracy: 0.9456, Val Loss: 0.1530, Val Accuracy: 0.9463 Epoch 428/1000, Train Loss: 0.1574, Train Accuracy: 0.9458, Val Loss: 0.1528, Val Accuracy: 0.9465 Epoch 429/1000, Train Loss: 0.1563, Train Accuracy: 0.9474, Val Loss: 0.1524, Val Accuracy: 0.9463 Epoch 430/1000, Train Loss: 0.1560, Train Accuracy: 0.9465, Val Loss: 0.1521, Val Accuracy: 0.9463 Epoch 431/1000, Train Loss: 0.1558, Train Accuracy: 0.9461, Val Loss: 0.1517, Val Accuracy: 0.9463 Epoch 432/1000, Train Loss: 0.1573, Train Accuracy: 0.9451, Val Loss: 0.1515, Val Accuracy: 0.9465 Epoch 433/1000, Train Loss: 0.1536, Train Accuracy: 0.9472, Val Loss: 0.1512, Val Accuracy: 0.9466 Epoch 434/1000, Train Loss: 0.1547, Train Accuracy: 0.9469, Val Loss: 0.1509, Val Accuracy: 0.9465 Epoch 435/1000, Train Loss: 0.1543, Train Accuracy: 0.9469, Val Loss: 0.1506, Val Accuracy: 0.9465 Epoch 436/1000, Train Loss: 0.1534, Train Accuracy: 0.9472, Val Loss: 0.1503, Val Accuracy: 0.9465 Epoch 437/1000, Train Loss: 0.1547, Train Accuracy: 0.9448, Val Loss: 0.1499, Val Accuracy: 0.9465 Epoch 438/1000, Train Loss: 0.1530, Train Accuracy: 0.9469, Val Loss: 0.1495, Val Accuracy: 0.9465 Epoch 439/1000, Train Loss: 0.1528, Train Accuracy: 0.9464, Val Loss: 0.1493, Val Accuracy: 0.9463 Epoch 440/1000, Train Loss: 0.1525, Train Accuracy: 0.9467, Val Loss: 0.1490, Val Accuracy: 0.9463 Epoch 441/1000, Train Loss: 0.1510, Train Accuracy: 0.9477, Val Loss: 0.1486, Val Accuracy: 0.9463 Epoch 442/1000, Train Loss: 0.1532, Train Accuracy: 0.9472, Val Loss: 0.1483, Val Accuracy: 0.9463 Epoch 443/1000, Train Loss: 0.1516, Train Accuracy: 0.9475, Val Loss: 0.1480, Val Accuracy: 0.9461 Epoch 444/1000, Train Loss: 0.1505, Train Accuracy: 0.9468, Val Loss: 0.1477, Val Accuracy: 0.9463 Epoch 445/1000, Train Loss: 0.1506, Train Accuracy: 0.9489, Val Loss: 0.1475, Val Accuracy: 0.9463 Epoch 446/1000, Train Loss: 0.1509, Train Accuracy: 0.9475, Val Loss: 0.1472, Val Accuracy: 0.9463 Epoch 447/1000, Train Loss: 0.1508, Train Accuracy: 0.9476, Val Loss: 0.1469, Val Accuracy: 0.9466 Epoch 448/1000, Train Loss: 0.1497, Train Accuracy: 0.9477, Val Loss: 0.1466, Val Accuracy: 0.9468 Epoch 449/1000, Train Loss: 0.1503, Train Accuracy: 0.9482, Val Loss: 0.1463, Val Accuracy: 0.9468 Epoch 450/1000, Train Loss: 0.1494, Train Accuracy: 0.9488, Val Loss: 0.1460, Val Accuracy: 0.9468 Epoch 451/1000, Train Loss: 0.1475, Train Accuracy: 0.9494, Val Loss: 0.1457, Val Accuracy: 0.9470 Epoch 452/1000, Train Loss: 0.1491, Train Accuracy: 0.9475, Val Loss: 0.1453, Val Accuracy: 0.9478 Epoch 453/1000, Train Loss: 0.1497, Train Accuracy: 0.9480, Val Loss: 0.1451, Val Accuracy: 0.9473 Epoch 454/1000, Train Loss: 0.1489, Train Accuracy: 0.9481, Val Loss: 0.1448, Val Accuracy: 0.9473 Epoch 455/1000, Train Loss: 0.1469, Train Accuracy: 0.9484, Val Loss: 0.1445, Val Accuracy: 0.9477 Epoch 456/1000, Train Loss: 0.1478, Train Accuracy: 0.9485, Val Loss: 0.1442, Val Accuracy: 0.9478 Epoch 457/1000, Train Loss: 0.1485, Train Accuracy: 0.9484, Val Loss: 0.1440, Val Accuracy: 0.9478 Epoch 458/1000, Train Loss: 0.1466, Train Accuracy: 0.9502, Val Loss: 0.1438, Val Accuracy: 0.9477 Epoch 459/1000, Train Loss: 0.1452, Train Accuracy: 0.9495, Val Loss: 0.1435, Val Accuracy: 0.9478 Epoch 460/1000, Train Loss: 0.1469, Train Accuracy: 0.9486, Val Loss: 0.1434, Val Accuracy: 0.9477 Epoch 461/1000, Train Loss: 0.1469, Train Accuracy: 0.9478, Val Loss: 0.1431, Val Accuracy: 0.9478 Epoch 462/1000, Train Loss: 0.1456, Train Accuracy: 0.9490, Val Loss: 0.1428, Val Accuracy: 0.9477 Epoch 463/1000, Train Loss: 0.1441, Train Accuracy: 0.9499, Val Loss: 0.1425, Val Accuracy: 0.9477 Epoch 464/1000, Train Loss: 0.1450, Train Accuracy: 0.9489, Val Loss: 0.1423, Val Accuracy: 0.9478 Epoch 465/1000, Train Loss: 0.1436, Train Accuracy: 0.9497, Val Loss: 0.1421, Val Accuracy: 0.9478 Epoch 466/1000, Train Loss: 0.1453, Train Accuracy: 0.9484, Val Loss: 0.1418, Val Accuracy: 0.9482 Epoch 467/1000, Train Loss: 0.1427, Train Accuracy: 0.9496, Val Loss: 0.1415, Val Accuracy: 0.9485 Epoch 468/1000, Train Loss: 0.1428, Train Accuracy: 0.9494, Val Loss: 0.1413, Val Accuracy: 0.9485 Epoch 469/1000, Train Loss: 0.1447, Train Accuracy: 0.9480, Val Loss: 0.1410, Val Accuracy: 0.9485 Epoch 470/1000, Train Loss: 0.1423, Train Accuracy: 0.9512, Val Loss: 0.1407, Val Accuracy: 0.9487 Epoch 471/1000, Train Loss: 0.1439, Train Accuracy: 0.9503, Val Loss: 0.1406, Val Accuracy: 0.9485 Epoch 472/1000, Train Loss: 0.1422, Train Accuracy: 0.9494, Val Loss: 0.1403, Val Accuracy: 0.9489 Epoch 473/1000, Train Loss: 0.1404, Train Accuracy: 0.9509, Val Loss: 0.1400, Val Accuracy: 0.9489 Epoch 474/1000, Train Loss: 0.1423, Train Accuracy: 0.9510, Val Loss: 0.1397, Val Accuracy: 0.9487 Epoch 475/1000, Train Loss: 0.1421, Train Accuracy: 0.9502, Val Loss: 0.1395, Val Accuracy: 0.9491 Epoch 476/1000, Train Loss: 0.1411, Train Accuracy: 0.9491, Val Loss: 0.1393, Val Accuracy: 0.9491 Epoch 477/1000, Train Loss: 0.1402, Train Accuracy: 0.9515, Val Loss: 0.1390, Val Accuracy: 0.9492 Epoch 478/1000, Train Loss: 0.1402, Train Accuracy: 0.9514, Val Loss: 0.1387, Val Accuracy: 0.9494 Epoch 479/1000, Train Loss: 0.1387, Train Accuracy: 0.9511, Val Loss: 0.1385, Val Accuracy: 0.9496 Epoch 480/1000, Train Loss: 0.1401, Train Accuracy: 0.9507, Val Loss: 0.1382, Val Accuracy: 0.9492 Epoch 481/1000, Train Loss: 0.1389, Train Accuracy: 0.9500, Val Loss: 0.1380, Val Accuracy: 0.9494 Epoch 482/1000, Train Loss: 0.1392, Train Accuracy: 0.9523, Val Loss: 0.1379, Val Accuracy: 0.9497 Epoch 483/1000, Train Loss: 0.1394, Train Accuracy: 0.9518, Val Loss: 0.1377, Val Accuracy: 0.9499 Epoch 484/1000, Train Loss: 0.1398, Train Accuracy: 0.9506, Val Loss: 0.1376, Val Accuracy: 0.9499 Epoch 485/1000, Train Loss: 0.1369, Train Accuracy: 0.9518, Val Loss: 0.1373, Val Accuracy: 0.9497 Epoch 486/1000, Train Loss: 0.1383, Train Accuracy: 0.9506, Val Loss: 0.1371, Val Accuracy: 0.9497 Epoch 487/1000, Train Loss: 0.1378, Train Accuracy: 0.9526, Val Loss: 0.1369, Val Accuracy: 0.9499 Epoch 488/1000, Train Loss: 0.1365, Train Accuracy: 0.9519, Val Loss: 0.1367, Val Accuracy: 0.9497 Epoch 489/1000, Train Loss: 0.1374, Train Accuracy: 0.9520, Val Loss: 0.1365, Val Accuracy: 0.9496 Epoch 490/1000, Train Loss: 0.1359, Train Accuracy: 0.9523, Val Loss: 0.1361, Val Accuracy: 0.9501 Epoch 491/1000, Train Loss: 0.1373, Train Accuracy: 0.9513, Val Loss: 0.1360, Val Accuracy: 0.9499 Epoch 492/1000, Train Loss: 0.1360, Train Accuracy: 0.9515, Val Loss: 0.1357, Val Accuracy: 0.9503 Epoch 493/1000, Train Loss: 0.1353, Train Accuracy: 0.9523, Val Loss: 0.1355, Val Accuracy: 0.9503 Epoch 494/1000, Train Loss: 0.1348, Train Accuracy: 0.9525, Val Loss: 0.1353, Val Accuracy: 0.9503 Epoch 495/1000, Train Loss: 0.1359, Train Accuracy: 0.9530, Val Loss: 0.1351, Val Accuracy: 0.9503 Epoch 496/1000, Train Loss: 0.1357, Train Accuracy: 0.9513, Val Loss: 0.1348, Val Accuracy: 0.9503 Epoch 497/1000, Train Loss: 0.1344, Train Accuracy: 0.9532, Val Loss: 0.1347, Val Accuracy: 0.9503 Epoch 498/1000, Train Loss: 0.1352, Train Accuracy: 0.9524, Val Loss: 0.1345, Val Accuracy: 0.9503 Epoch 499/1000, Train Loss: 0.1327, Train Accuracy: 0.9541, Val Loss: 0.1340, Val Accuracy: 0.9511 Epoch 500/1000, Train Loss: 0.1323, Train Accuracy: 0.9542, Val Loss: 0.1340, Val Accuracy: 0.9508 Epoch 501/1000, Train Loss: 0.1335, Train Accuracy: 0.9533, Val Loss: 0.1337, Val Accuracy: 0.9511 Epoch 502/1000, Train Loss: 0.1335, Train Accuracy: 0.9529, Val Loss: 0.1335, Val Accuracy: 0.9515 Epoch 503/1000, Train Loss: 0.1326, Train Accuracy: 0.9534, Val Loss: 0.1334, Val Accuracy: 0.9513 Epoch 504/1000, Train Loss: 0.1330, Train Accuracy: 0.9524, Val Loss: 0.1332, Val Accuracy: 0.9513 Epoch 505/1000, Train Loss: 0.1323, Train Accuracy: 0.9530, Val Loss: 0.1330, Val Accuracy: 0.9515 Epoch 506/1000, Train Loss: 0.1328, Train Accuracy: 0.9538, Val Loss: 0.1328, Val Accuracy: 0.9515 Epoch 507/1000, Train Loss: 0.1324, Train Accuracy: 0.9540, Val Loss: 0.1326, Val Accuracy: 0.9511 Epoch 508/1000, Train Loss: 0.1306, Train Accuracy: 0.9524, Val Loss: 0.1325, Val Accuracy: 0.9515 Epoch 509/1000, Train Loss: 0.1299, Train Accuracy: 0.9548, Val Loss: 0.1322, Val Accuracy: 0.9515 Epoch 510/1000, Train Loss: 0.1311, Train Accuracy: 0.9542, Val Loss: 0.1319, Val Accuracy: 0.9515 Epoch 511/1000, Train Loss: 0.1302, Train Accuracy: 0.9559, Val Loss: 0.1318, Val Accuracy: 0.9515 Epoch 512/1000, Train Loss: 0.1286, Train Accuracy: 0.9545, Val Loss: 0.1315, Val Accuracy: 0.9516 Epoch 513/1000, Train Loss: 0.1319, Train Accuracy: 0.9527, Val Loss: 0.1314, Val Accuracy: 0.9516 Epoch 514/1000, Train Loss: 0.1290, Train Accuracy: 0.9542, Val Loss: 0.1312, Val Accuracy: 0.9518 Epoch 515/1000, Train Loss: 0.1310, Train Accuracy: 0.9542, Val Loss: 0.1309, Val Accuracy: 0.9518 Epoch 516/1000, Train Loss: 0.1299, Train Accuracy: 0.9528, Val Loss: 0.1308, Val Accuracy: 0.9520 Epoch 517/1000, Train Loss: 0.1305, Train Accuracy: 0.9527, Val Loss: 0.1307, Val Accuracy: 0.9522 Epoch 518/1000, Train Loss: 0.1302, Train Accuracy: 0.9527, Val Loss: 0.1304, Val Accuracy: 0.9522 Epoch 519/1000, Train Loss: 0.1280, Train Accuracy: 0.9561, Val Loss: 0.1303, Val Accuracy: 0.9520 Epoch 520/1000, Train Loss: 0.1270, Train Accuracy: 0.9563, Val Loss: 0.1301, Val Accuracy: 0.9523 Epoch 521/1000, Train Loss: 0.1278, Train Accuracy: 0.9551, Val Loss: 0.1299, Val Accuracy: 0.9525 Epoch 522/1000, Train Loss: 0.1284, Train Accuracy: 0.9551, Val Loss: 0.1297, Val Accuracy: 0.9522 Epoch 523/1000, Train Loss: 0.1269, Train Accuracy: 0.9561, Val Loss: 0.1296, Val Accuracy: 0.9525 Epoch 524/1000, Train Loss: 0.1274, Train Accuracy: 0.9559, Val Loss: 0.1295, Val Accuracy: 0.9525 Epoch 525/1000, Train Loss: 0.1267, Train Accuracy: 0.9562, Val Loss: 0.1292, Val Accuracy: 0.9525 Epoch 526/1000, Train Loss: 0.1283, Train Accuracy: 0.9544, Val Loss: 0.1291, Val Accuracy: 0.9525 Epoch 527/1000, Train Loss: 0.1250, Train Accuracy: 0.9556, Val Loss: 0.1289, Val Accuracy: 0.9523 Epoch 528/1000, Train Loss: 0.1279, Train Accuracy: 0.9555, Val Loss: 0.1288, Val Accuracy: 0.9523 Epoch 529/1000, Train Loss: 0.1273, Train Accuracy: 0.9564, Val Loss: 0.1285, Val Accuracy: 0.9523 Epoch 530/1000, Train Loss: 0.1247, Train Accuracy: 0.9570, Val Loss: 0.1283, Val Accuracy: 0.9522 Epoch 531/1000, Train Loss: 0.1247, Train Accuracy: 0.9567, Val Loss: 0.1281, Val Accuracy: 0.9522 Epoch 532/1000, Train Loss: 0.1247, Train Accuracy: 0.9568, Val Loss: 0.1280, Val Accuracy: 0.9520 Epoch 533/1000, Train Loss: 0.1258, Train Accuracy: 0.9552, Val Loss: 0.1279, Val Accuracy: 0.9522 Epoch 534/1000, Train Loss: 0.1251, Train Accuracy: 0.9574, Val Loss: 0.1276, Val Accuracy: 0.9523 Epoch 535/1000, Train Loss: 0.1237, Train Accuracy: 0.9568, Val Loss: 0.1275, Val Accuracy: 0.9527 Epoch 536/1000, Train Loss: 0.1252, Train Accuracy: 0.9570, Val Loss: 0.1272, Val Accuracy: 0.9527 Epoch 537/1000, Train Loss: 0.1242, Train Accuracy: 0.9567, Val Loss: 0.1270, Val Accuracy: 0.9530 Epoch 538/1000, Train Loss: 0.1227, Train Accuracy: 0.9569, Val Loss: 0.1269, Val Accuracy: 0.9527 Epoch 539/1000, Train Loss: 0.1244, Train Accuracy: 0.9571, Val Loss: 0.1267, Val Accuracy: 0.9530 Epoch 540/1000, Train Loss: 0.1239, Train Accuracy: 0.9562, Val Loss: 0.1267, Val Accuracy: 0.9530 Epoch 541/1000, Train Loss: 0.1224, Train Accuracy: 0.9560, Val Loss: 0.1264, Val Accuracy: 0.9527 Epoch 542/1000, Train Loss: 0.1242, Train Accuracy: 0.9560, Val Loss: 0.1262, Val Accuracy: 0.9528 Epoch 543/1000, Train Loss: 0.1245, Train Accuracy: 0.9564, Val Loss: 0.1261, Val Accuracy: 0.9527 Epoch 544/1000, Train Loss: 0.1227, Train Accuracy: 0.9580, Val Loss: 0.1260, Val Accuracy: 0.9527 Epoch 545/1000, Train Loss: 0.1232, Train Accuracy: 0.9564, Val Loss: 0.1258, Val Accuracy: 0.9528 Epoch 546/1000, Train Loss: 0.1228, Train Accuracy: 0.9570, Val Loss: 0.1257, Val Accuracy: 0.9534 Epoch 547/1000, Train Loss: 0.1210, Train Accuracy: 0.9582, Val Loss: 0.1256, Val Accuracy: 0.9532 Epoch 548/1000, Train Loss: 0.1214, Train Accuracy: 0.9572, Val Loss: 0.1254, Val Accuracy: 0.9534 Epoch 549/1000, Train Loss: 0.1210, Train Accuracy: 0.9570, Val Loss: 0.1251, Val Accuracy: 0.9532 Epoch 550/1000, Train Loss: 0.1217, Train Accuracy: 0.9586, Val Loss: 0.1252, Val Accuracy: 0.9537 Epoch 551/1000, Train Loss: 0.1223, Train Accuracy: 0.9569, Val Loss: 0.1248, Val Accuracy: 0.9539 Epoch 552/1000, Train Loss: 0.1206, Train Accuracy: 0.9570, Val Loss: 0.1249, Val Accuracy: 0.9535 Epoch 553/1000, Train Loss: 0.1211, Train Accuracy: 0.9570, Val Loss: 0.1246, Val Accuracy: 0.9539 Epoch 554/1000, Train Loss: 0.1214, Train Accuracy: 0.9577, Val Loss: 0.1245, Val Accuracy: 0.9535 Epoch 555/1000, Train Loss: 0.1205, Train Accuracy: 0.9577, Val Loss: 0.1242, Val Accuracy: 0.9539 Epoch 556/1000, Train Loss: 0.1184, Train Accuracy: 0.9588, Val Loss: 0.1241, Val Accuracy: 0.9537 Epoch 557/1000, Train Loss: 0.1204, Train Accuracy: 0.9570, Val Loss: 0.1239, Val Accuracy: 0.9539 Epoch 558/1000, Train Loss: 0.1200, Train Accuracy: 0.9583, Val Loss: 0.1238, Val Accuracy: 0.9540 Epoch 559/1000, Train Loss: 0.1186, Train Accuracy: 0.9585, Val Loss: 0.1237, Val Accuracy: 0.9540 Epoch 560/1000, Train Loss: 0.1189, Train Accuracy: 0.9577, Val Loss: 0.1237, Val Accuracy: 0.9539 Epoch 561/1000, Train Loss: 0.1184, Train Accuracy: 0.9586, Val Loss: 0.1235, Val Accuracy: 0.9539 Epoch 562/1000, Train Loss: 0.1189, Train Accuracy: 0.9584, Val Loss: 0.1233, Val Accuracy: 0.9542 Epoch 563/1000, Train Loss: 0.1204, Train Accuracy: 0.9582, Val Loss: 0.1232, Val Accuracy: 0.9542 Epoch 564/1000, Train Loss: 0.1165, Train Accuracy: 0.9591, Val Loss: 0.1230, Val Accuracy: 0.9542 Epoch 565/1000, Train Loss: 0.1186, Train Accuracy: 0.9580, Val Loss: 0.1230, Val Accuracy: 0.9542 Epoch 566/1000, Train Loss: 0.1192, Train Accuracy: 0.9580, Val Loss: 0.1226, Val Accuracy: 0.9542 Epoch 567/1000, Train Loss: 0.1173, Train Accuracy: 0.9594, Val Loss: 0.1227, Val Accuracy: 0.9540 Epoch 568/1000, Train Loss: 0.1182, Train Accuracy: 0.9580, Val Loss: 0.1224, Val Accuracy: 0.9546 Epoch 569/1000, Train Loss: 0.1152, Train Accuracy: 0.9586, Val Loss: 0.1222, Val Accuracy: 0.9549 Epoch 570/1000, Train Loss: 0.1172, Train Accuracy: 0.9589, Val Loss: 0.1221, Val Accuracy: 0.9547 Epoch 571/1000, Train Loss: 0.1159, Train Accuracy: 0.9594, Val Loss: 0.1221, Val Accuracy: 0.9546 Epoch 572/1000, Train Loss: 0.1174, Train Accuracy: 0.9567, Val Loss: 0.1218, Val Accuracy: 0.9547 Epoch 573/1000, Train Loss: 0.1176, Train Accuracy: 0.9579, Val Loss: 0.1218, Val Accuracy: 0.9546 Epoch 574/1000, Train Loss: 0.1150, Train Accuracy: 0.9596, Val Loss: 0.1216, Val Accuracy: 0.9546 Epoch 575/1000, Train Loss: 0.1135, Train Accuracy: 0.9609, Val Loss: 0.1215, Val Accuracy: 0.9546 Epoch 576/1000, Train Loss: 0.1155, Train Accuracy: 0.9597, Val Loss: 0.1213, Val Accuracy: 0.9546 Epoch 577/1000, Train Loss: 0.1168, Train Accuracy: 0.9589, Val Loss: 0.1210, Val Accuracy: 0.9547 Epoch 578/1000, Train Loss: 0.1151, Train Accuracy: 0.9597, Val Loss: 0.1208, Val Accuracy: 0.9547 Epoch 579/1000, Train Loss: 0.1157, Train Accuracy: 0.9605, Val Loss: 0.1208, Val Accuracy: 0.9547 Epoch 580/1000, Train Loss: 0.1140, Train Accuracy: 0.9594, Val Loss: 0.1207, Val Accuracy: 0.9546 Epoch 581/1000, Train Loss: 0.1127, Train Accuracy: 0.9607, Val Loss: 0.1205, Val Accuracy: 0.9544 Epoch 582/1000, Train Loss: 0.1150, Train Accuracy: 0.9599, Val Loss: 0.1204, Val Accuracy: 0.9544 Epoch 583/1000, Train Loss: 0.1138, Train Accuracy: 0.9601, Val Loss: 0.1203, Val Accuracy: 0.9542 Epoch 584/1000, Train Loss: 0.1150, Train Accuracy: 0.9600, Val Loss: 0.1202, Val Accuracy: 0.9546 Epoch 585/1000, Train Loss: 0.1138, Train Accuracy: 0.9601, Val Loss: 0.1198, Val Accuracy: 0.9546 Epoch 586/1000, Train Loss: 0.1151, Train Accuracy: 0.9595, Val Loss: 0.1197, Val Accuracy: 0.9544 Epoch 587/1000, Train Loss: 0.1132, Train Accuracy: 0.9607, Val Loss: 0.1196, Val Accuracy: 0.9546 Epoch 588/1000, Train Loss: 0.1135, Train Accuracy: 0.9607, Val Loss: 0.1195, Val Accuracy: 0.9546 Epoch 589/1000, Train Loss: 0.1138, Train Accuracy: 0.9597, Val Loss: 0.1192, Val Accuracy: 0.9547 Epoch 590/1000, Train Loss: 0.1127, Train Accuracy: 0.9617, Val Loss: 0.1190, Val Accuracy: 0.9547 Epoch 591/1000, Train Loss: 0.1116, Train Accuracy: 0.9606, Val Loss: 0.1189, Val Accuracy: 0.9547 Epoch 592/1000, Train Loss: 0.1132, Train Accuracy: 0.9595, Val Loss: 0.1188, Val Accuracy: 0.9549 Epoch 593/1000, Train Loss: 0.1110, Train Accuracy: 0.9614, Val Loss: 0.1187, Val Accuracy: 0.9549 Epoch 594/1000, Train Loss: 0.1117, Train Accuracy: 0.9609, Val Loss: 0.1186, Val Accuracy: 0.9551 Epoch 595/1000, Train Loss: 0.1116, Train Accuracy: 0.9607, Val Loss: 0.1185, Val Accuracy: 0.9551 Epoch 596/1000, Train Loss: 0.1124, Train Accuracy: 0.9601, Val Loss: 0.1185, Val Accuracy: 0.9551 Epoch 597/1000, Train Loss: 0.1106, Train Accuracy: 0.9627, Val Loss: 0.1184, Val Accuracy: 0.9554 Epoch 598/1000, Train Loss: 0.1112, Train Accuracy: 0.9611, Val Loss: 0.1183, Val Accuracy: 0.9556 Epoch 599/1000, Train Loss: 0.1131, Train Accuracy: 0.9599, Val Loss: 0.1181, Val Accuracy: 0.9561 Epoch 600/1000, Train Loss: 0.1113, Train Accuracy: 0.9611, Val Loss: 0.1181, Val Accuracy: 0.9559 Epoch 601/1000, Train Loss: 0.1116, Train Accuracy: 0.9602, Val Loss: 0.1180, Val Accuracy: 0.9559 Epoch 602/1000, Train Loss: 0.1097, Train Accuracy: 0.9620, Val Loss: 0.1180, Val Accuracy: 0.9558 Epoch 603/1000, Train Loss: 0.1109, Train Accuracy: 0.9607, Val Loss: 0.1177, Val Accuracy: 0.9554 Epoch 604/1000, Train Loss: 0.1102, Train Accuracy: 0.9622, Val Loss: 0.1175, Val Accuracy: 0.9558 Epoch 605/1000, Train Loss: 0.1101, Train Accuracy: 0.9612, Val Loss: 0.1174, Val Accuracy: 0.9559 Epoch 606/1000, Train Loss: 0.1087, Train Accuracy: 0.9621, Val Loss: 0.1172, Val Accuracy: 0.9559 Epoch 607/1000, Train Loss: 0.1093, Train Accuracy: 0.9619, Val Loss: 0.1172, Val Accuracy: 0.9559 Epoch 608/1000, Train Loss: 0.1073, Train Accuracy: 0.9638, Val Loss: 0.1171, Val Accuracy: 0.9561 Epoch 609/1000, Train Loss: 0.1084, Train Accuracy: 0.9626, Val Loss: 0.1169, Val Accuracy: 0.9559 Epoch 610/1000, Train Loss: 0.1088, Train Accuracy: 0.9626, Val Loss: 0.1169, Val Accuracy: 0.9559 Epoch 611/1000, Train Loss: 0.1080, Train Accuracy: 0.9617, Val Loss: 0.1169, Val Accuracy: 0.9561 Epoch 612/1000, Train Loss: 0.1078, Train Accuracy: 0.9619, Val Loss: 0.1167, Val Accuracy: 0.9559 Epoch 613/1000, Train Loss: 0.1067, Train Accuracy: 0.9636, Val Loss: 0.1166, Val Accuracy: 0.9558 Epoch 614/1000, Train Loss: 0.1092, Train Accuracy: 0.9625, Val Loss: 0.1163, Val Accuracy: 0.9558 Epoch 615/1000, Train Loss: 0.1081, Train Accuracy: 0.9620, Val Loss: 0.1163, Val Accuracy: 0.9561 Epoch 616/1000, Train Loss: 0.1074, Train Accuracy: 0.9627, Val Loss: 0.1163, Val Accuracy: 0.9559 Epoch 617/1000, Train Loss: 0.1081, Train Accuracy: 0.9621, Val Loss: 0.1160, Val Accuracy: 0.9561 Epoch 618/1000, Train Loss: 0.1038, Train Accuracy: 0.9646, Val Loss: 0.1157, Val Accuracy: 0.9558 Epoch 619/1000, Train Loss: 0.1086, Train Accuracy: 0.9606, Val Loss: 0.1157, Val Accuracy: 0.9559 Epoch 620/1000, Train Loss: 0.1069, Train Accuracy: 0.9633, Val Loss: 0.1157, Val Accuracy: 0.9559 Epoch 621/1000, Train Loss: 0.1067, Train Accuracy: 0.9623, Val Loss: 0.1156, Val Accuracy: 0.9558 Epoch 622/1000, Train Loss: 0.1077, Train Accuracy: 0.9626, Val Loss: 0.1155, Val Accuracy: 0.9561 Epoch 623/1000, Train Loss: 0.1062, Train Accuracy: 0.9626, Val Loss: 0.1155, Val Accuracy: 0.9559 Epoch 624/1000, Train Loss: 0.1062, Train Accuracy: 0.9627, Val Loss: 0.1151, Val Accuracy: 0.9561 Epoch 625/1000, Train Loss: 0.1066, Train Accuracy: 0.9624, Val Loss: 0.1150, Val Accuracy: 0.9563 Epoch 626/1000, Train Loss: 0.1056, Train Accuracy: 0.9632, Val Loss: 0.1148, Val Accuracy: 0.9561 Epoch 627/1000, Train Loss: 0.1076, Train Accuracy: 0.9616, Val Loss: 0.1147, Val Accuracy: 0.9561 Epoch 628/1000, Train Loss: 0.1046, Train Accuracy: 0.9646, Val Loss: 0.1145, Val Accuracy: 0.9563 Epoch 629/1000, Train Loss: 0.1057, Train Accuracy: 0.9629, Val Loss: 0.1147, Val Accuracy: 0.9563 Epoch 630/1000, Train Loss: 0.1065, Train Accuracy: 0.9617, Val Loss: 0.1145, Val Accuracy: 0.9563 Epoch 631/1000, Train Loss: 0.1042, Train Accuracy: 0.9629, Val Loss: 0.1143, Val Accuracy: 0.9561 Epoch 632/1000, Train Loss: 0.1047, Train Accuracy: 0.9631, Val Loss: 0.1144, Val Accuracy: 0.9565 Epoch 633/1000, Train Loss: 0.1060, Train Accuracy: 0.9625, Val Loss: 0.1143, Val Accuracy: 0.9566 Epoch 634/1000, Train Loss: 0.1042, Train Accuracy: 0.9632, Val Loss: 0.1141, Val Accuracy: 0.9566 Epoch 635/1000, Train Loss: 0.1042, Train Accuracy: 0.9645, Val Loss: 0.1138, Val Accuracy: 0.9565 Epoch 636/1000, Train Loss: 0.1036, Train Accuracy: 0.9634, Val Loss: 0.1139, Val Accuracy: 0.9566 Epoch 637/1000, Train Loss: 0.1025, Train Accuracy: 0.9641, Val Loss: 0.1138, Val Accuracy: 0.9566 Epoch 638/1000, Train Loss: 0.1048, Train Accuracy: 0.9632, Val Loss: 0.1136, Val Accuracy: 0.9570 Epoch 639/1000, Train Loss: 0.1043, Train Accuracy: 0.9629, Val Loss: 0.1134, Val Accuracy: 0.9568 Epoch 640/1000, Train Loss: 0.1024, Train Accuracy: 0.9647, Val Loss: 0.1133, Val Accuracy: 0.9568 Epoch 641/1000, Train Loss: 0.1034, Train Accuracy: 0.9642, Val Loss: 0.1131, Val Accuracy: 0.9566 Epoch 642/1000, Train Loss: 0.1018, Train Accuracy: 0.9649, Val Loss: 0.1130, Val Accuracy: 0.9566 Epoch 643/1000, Train Loss: 0.1015, Train Accuracy: 0.9641, Val Loss: 0.1130, Val Accuracy: 0.9566 Epoch 644/1000, Train Loss: 0.1020, Train Accuracy: 0.9643, Val Loss: 0.1132, Val Accuracy: 0.9563 Epoch 645/1000, Train Loss: 0.1034, Train Accuracy: 0.9641, Val Loss: 0.1130, Val Accuracy: 0.9563 Epoch 646/1000, Train Loss: 0.1021, Train Accuracy: 0.9657, Val Loss: 0.1127, Val Accuracy: 0.9565 Epoch 647/1000, Train Loss: 0.1011, Train Accuracy: 0.9647, Val Loss: 0.1129, Val Accuracy: 0.9563 Epoch 648/1000, Train Loss: 0.1010, Train Accuracy: 0.9645, Val Loss: 0.1126, Val Accuracy: 0.9566 Epoch 649/1000, Train Loss: 0.1024, Train Accuracy: 0.9650, Val Loss: 0.1123, Val Accuracy: 0.9566 Epoch 650/1000, Train Loss: 0.1035, Train Accuracy: 0.9626, Val Loss: 0.1122, Val Accuracy: 0.9568 Epoch 651/1000, Train Loss: 0.1014, Train Accuracy: 0.9640, Val Loss: 0.1122, Val Accuracy: 0.9568 Epoch 652/1000, Train Loss: 0.1005, Train Accuracy: 0.9648, Val Loss: 0.1122, Val Accuracy: 0.9561 Epoch 653/1000, Train Loss: 0.1016, Train Accuracy: 0.9631, Val Loss: 0.1119, Val Accuracy: 0.9566 Epoch 654/1000, Train Loss: 0.0999, Train Accuracy: 0.9647, Val Loss: 0.1118, Val Accuracy: 0.9568 Epoch 655/1000, Train Loss: 0.1025, Train Accuracy: 0.9649, Val Loss: 0.1117, Val Accuracy: 0.9568 Epoch 656/1000, Train Loss: 0.1007, Train Accuracy: 0.9641, Val Loss: 0.1116, Val Accuracy: 0.9566 Epoch 657/1000, Train Loss: 0.1014, Train Accuracy: 0.9636, Val Loss: 0.1116, Val Accuracy: 0.9565 Epoch 658/1000, Train Loss: 0.1004, Train Accuracy: 0.9651, Val Loss: 0.1114, Val Accuracy: 0.9566 Epoch 659/1000, Train Loss: 0.0984, Train Accuracy: 0.9666, Val Loss: 0.1114, Val Accuracy: 0.9565 Epoch 660/1000, Train Loss: 0.0997, Train Accuracy: 0.9632, Val Loss: 0.1113, Val Accuracy: 0.9568 Epoch 661/1000, Train Loss: 0.0993, Train Accuracy: 0.9657, Val Loss: 0.1111, Val Accuracy: 0.9568 Epoch 662/1000, Train Loss: 0.0991, Train Accuracy: 0.9662, Val Loss: 0.1109, Val Accuracy: 0.9573 Epoch 663/1000, Train Loss: 0.0998, Train Accuracy: 0.9654, Val Loss: 0.1110, Val Accuracy: 0.9570 Epoch 664/1000, Train Loss: 0.0993, Train Accuracy: 0.9658, Val Loss: 0.1108, Val Accuracy: 0.9570 Epoch 665/1000, Train Loss: 0.0994, Train Accuracy: 0.9640, Val Loss: 0.1106, Val Accuracy: 0.9570 Epoch 666/1000, Train Loss: 0.0973, Train Accuracy: 0.9648, Val Loss: 0.1105, Val Accuracy: 0.9570 Epoch 667/1000, Train Loss: 0.0976, Train Accuracy: 0.9663, Val Loss: 0.1104, Val Accuracy: 0.9571 Epoch 668/1000, Train Loss: 0.0991, Train Accuracy: 0.9650, Val Loss: 0.1103, Val Accuracy: 0.9570 Epoch 669/1000, Train Loss: 0.0982, Train Accuracy: 0.9654, Val Loss: 0.1103, Val Accuracy: 0.9573 Epoch 670/1000, Train Loss: 0.0976, Train Accuracy: 0.9655, Val Loss: 0.1103, Val Accuracy: 0.9571 Epoch 671/1000, Train Loss: 0.0982, Train Accuracy: 0.9651, Val Loss: 0.1100, Val Accuracy: 0.9571 Epoch 672/1000, Train Loss: 0.0977, Train Accuracy: 0.9659, Val Loss: 0.1100, Val Accuracy: 0.9570 Epoch 673/1000, Train Loss: 0.0981, Train Accuracy: 0.9657, Val Loss: 0.1099, Val Accuracy: 0.9571 Epoch 674/1000, Train Loss: 0.0995, Train Accuracy: 0.9646, Val Loss: 0.1098, Val Accuracy: 0.9573 Epoch 675/1000, Train Loss: 0.0956, Train Accuracy: 0.9676, Val Loss: 0.1097, Val Accuracy: 0.9571 Epoch 676/1000, Train Loss: 0.0975, Train Accuracy: 0.9664, Val Loss: 0.1097, Val Accuracy: 0.9570 Epoch 677/1000, Train Loss: 0.0973, Train Accuracy: 0.9656, Val Loss: 0.1096, Val Accuracy: 0.9573 Epoch 678/1000, Train Loss: 0.0987, Train Accuracy: 0.9649, Val Loss: 0.1093, Val Accuracy: 0.9575 Epoch 679/1000, Train Loss: 0.0965, Train Accuracy: 0.9665, Val Loss: 0.1093, Val Accuracy: 0.9577 Epoch 680/1000, Train Loss: 0.0974, Train Accuracy: 0.9666, Val Loss: 0.1092, Val Accuracy: 0.9575 Epoch 681/1000, Train Loss: 0.0960, Train Accuracy: 0.9680, Val Loss: 0.1091, Val Accuracy: 0.9577 Epoch 682/1000, Train Loss: 0.0963, Train Accuracy: 0.9669, Val Loss: 0.1090, Val Accuracy: 0.9577 Epoch 683/1000, Train Loss: 0.0978, Train Accuracy: 0.9667, Val Loss: 0.1090, Val Accuracy: 0.9577 Epoch 684/1000, Train Loss: 0.0962, Train Accuracy: 0.9666, Val Loss: 0.1090, Val Accuracy: 0.9577 Epoch 685/1000, Train Loss: 0.0965, Train Accuracy: 0.9666, Val Loss: 0.1087, Val Accuracy: 0.9578 Epoch 686/1000, Train Loss: 0.0949, Train Accuracy: 0.9675, Val Loss: 0.1088, Val Accuracy: 0.9582 Epoch 687/1000, Train Loss: 0.0962, Train Accuracy: 0.9661, Val Loss: 0.1087, Val Accuracy: 0.9582 Epoch 688/1000, Train Loss: 0.0949, Train Accuracy: 0.9671, Val Loss: 0.1086, Val Accuracy: 0.9582 Epoch 689/1000, Train Loss: 0.0959, Train Accuracy: 0.9661, Val Loss: 0.1085, Val Accuracy: 0.9582 Epoch 690/1000, Train Loss: 0.0949, Train Accuracy: 0.9671, Val Loss: 0.1083, Val Accuracy: 0.9583 Epoch 691/1000, Train Loss: 0.0945, Train Accuracy: 0.9669, Val Loss: 0.1082, Val Accuracy: 0.9582 Epoch 692/1000, Train Loss: 0.0938, Train Accuracy: 0.9676, Val Loss: 0.1082, Val Accuracy: 0.9583 Epoch 693/1000, Train Loss: 0.0956, Train Accuracy: 0.9681, Val Loss: 0.1081, Val Accuracy: 0.9582 Epoch 694/1000, Train Loss: 0.0941, Train Accuracy: 0.9669, Val Loss: 0.1080, Val Accuracy: 0.9583 Epoch 695/1000, Train Loss: 0.0941, Train Accuracy: 0.9664, Val Loss: 0.1080, Val Accuracy: 0.9582 Epoch 696/1000, Train Loss: 0.0935, Train Accuracy: 0.9678, Val Loss: 0.1079, Val Accuracy: 0.9580 Epoch 697/1000, Train Loss: 0.0938, Train Accuracy: 0.9674, Val Loss: 0.1079, Val Accuracy: 0.9582 Epoch 698/1000, Train Loss: 0.0946, Train Accuracy: 0.9669, Val Loss: 0.1078, Val Accuracy: 0.9585 Epoch 699/1000, Train Loss: 0.0945, Train Accuracy: 0.9675, Val Loss: 0.1075, Val Accuracy: 0.9578 Epoch 700/1000, Train Loss: 0.0952, Train Accuracy: 0.9663, Val Loss: 0.1075, Val Accuracy: 0.9580 Epoch 701/1000, Train Loss: 0.0945, Train Accuracy: 0.9671, Val Loss: 0.1074, Val Accuracy: 0.9585 Epoch 702/1000, Train Loss: 0.0926, Train Accuracy: 0.9686, Val Loss: 0.1072, Val Accuracy: 0.9582 Epoch 703/1000, Train Loss: 0.0925, Train Accuracy: 0.9680, Val Loss: 0.1071, Val Accuracy: 0.9580 Epoch 704/1000, Train Loss: 0.0926, Train Accuracy: 0.9680, Val Loss: 0.1072, Val Accuracy: 0.9589 Epoch 705/1000, Train Loss: 0.0949, Train Accuracy: 0.9676, Val Loss: 0.1072, Val Accuracy: 0.9592 Epoch 706/1000, Train Loss: 0.0918, Train Accuracy: 0.9693, Val Loss: 0.1071, Val Accuracy: 0.9589 Epoch 707/1000, Train Loss: 0.0908, Train Accuracy: 0.9692, Val Loss: 0.1070, Val Accuracy: 0.9587 Epoch 708/1000, Train Loss: 0.0921, Train Accuracy: 0.9686, Val Loss: 0.1068, Val Accuracy: 0.9585 Epoch 709/1000, Train Loss: 0.0925, Train Accuracy: 0.9686, Val Loss: 0.1067, Val Accuracy: 0.9587 Epoch 710/1000, Train Loss: 0.0932, Train Accuracy: 0.9672, Val Loss: 0.1066, Val Accuracy: 0.9587 Epoch 711/1000, Train Loss: 0.0932, Train Accuracy: 0.9684, Val Loss: 0.1067, Val Accuracy: 0.9590 Epoch 712/1000, Train Loss: 0.0925, Train Accuracy: 0.9680, Val Loss: 0.1065, Val Accuracy: 0.9590 Epoch 713/1000, Train Loss: 0.0915, Train Accuracy: 0.9691, Val Loss: 0.1065, Val Accuracy: 0.9590 Epoch 714/1000, Train Loss: 0.0901, Train Accuracy: 0.9691, Val Loss: 0.1064, Val Accuracy: 0.9590 Epoch 715/1000, Train Loss: 0.0914, Train Accuracy: 0.9683, Val Loss: 0.1063, Val Accuracy: 0.9590 Epoch 716/1000, Train Loss: 0.0918, Train Accuracy: 0.9678, Val Loss: 0.1062, Val Accuracy: 0.9592 Epoch 717/1000, Train Loss: 0.0899, Train Accuracy: 0.9685, Val Loss: 0.1060, Val Accuracy: 0.9590 Epoch 718/1000, Train Loss: 0.0899, Train Accuracy: 0.9704, Val Loss: 0.1060, Val Accuracy: 0.9594 Epoch 719/1000, Train Loss: 0.0898, Train Accuracy: 0.9684, Val Loss: 0.1058, Val Accuracy: 0.9590 Epoch 720/1000, Train Loss: 0.0896, Train Accuracy: 0.9700, Val Loss: 0.1058, Val Accuracy: 0.9592 Epoch 721/1000, Train Loss: 0.0909, Train Accuracy: 0.9694, Val Loss: 0.1058, Val Accuracy: 0.9590 Epoch 722/1000, Train Loss: 0.0896, Train Accuracy: 0.9685, Val Loss: 0.1058, Val Accuracy: 0.9594 Epoch 723/1000, Train Loss: 0.0915, Train Accuracy: 0.9690, Val Loss: 0.1055, Val Accuracy: 0.9592 Epoch 724/1000, Train Loss: 0.0908, Train Accuracy: 0.9697, Val Loss: 0.1055, Val Accuracy: 0.9596 Epoch 725/1000, Train Loss: 0.0890, Train Accuracy: 0.9695, Val Loss: 0.1054, Val Accuracy: 0.9597 Epoch 726/1000, Train Loss: 0.0896, Train Accuracy: 0.9695, Val Loss: 0.1054, Val Accuracy: 0.9597 Epoch 727/1000, Train Loss: 0.0898, Train Accuracy: 0.9681, Val Loss: 0.1052, Val Accuracy: 0.9594 Epoch 728/1000, Train Loss: 0.0891, Train Accuracy: 0.9691, Val Loss: 0.1052, Val Accuracy: 0.9596 Epoch 729/1000, Train Loss: 0.0891, Train Accuracy: 0.9692, Val Loss: 0.1052, Val Accuracy: 0.9592 Epoch 730/1000, Train Loss: 0.0878, Train Accuracy: 0.9700, Val Loss: 0.1050, Val Accuracy: 0.9594 Epoch 731/1000, Train Loss: 0.0909, Train Accuracy: 0.9688, Val Loss: 0.1049, Val Accuracy: 0.9592 Epoch 732/1000, Train Loss: 0.0886, Train Accuracy: 0.9697, Val Loss: 0.1050, Val Accuracy: 0.9596 Epoch 733/1000, Train Loss: 0.0895, Train Accuracy: 0.9691, Val Loss: 0.1049, Val Accuracy: 0.9596 Epoch 734/1000, Train Loss: 0.0888, Train Accuracy: 0.9698, Val Loss: 0.1047, Val Accuracy: 0.9594 Epoch 735/1000, Train Loss: 0.0868, Train Accuracy: 0.9692, Val Loss: 0.1047, Val Accuracy: 0.9596 Epoch 736/1000, Train Loss: 0.0892, Train Accuracy: 0.9691, Val Loss: 0.1046, Val Accuracy: 0.9596 Epoch 737/1000, Train Loss: 0.0878, Train Accuracy: 0.9706, Val Loss: 0.1047, Val Accuracy: 0.9599 Epoch 738/1000, Train Loss: 0.0876, Train Accuracy: 0.9703, Val Loss: 0.1046, Val Accuracy: 0.9597 Epoch 739/1000, Train Loss: 0.0885, Train Accuracy: 0.9698, Val Loss: 0.1045, Val Accuracy: 0.9597 Epoch 740/1000, Train Loss: 0.0873, Train Accuracy: 0.9704, Val Loss: 0.1044, Val Accuracy: 0.9596 Epoch 741/1000, Train Loss: 0.0870, Train Accuracy: 0.9707, Val Loss: 0.1044, Val Accuracy: 0.9597 Epoch 742/1000, Train Loss: 0.0872, Train Accuracy: 0.9708, Val Loss: 0.1044, Val Accuracy: 0.9597 Epoch 743/1000, Train Loss: 0.0886, Train Accuracy: 0.9694, Val Loss: 0.1043, Val Accuracy: 0.9597 Epoch 744/1000, Train Loss: 0.0854, Train Accuracy: 0.9710, Val Loss: 0.1042, Val Accuracy: 0.9597 Epoch 745/1000, Train Loss: 0.0888, Train Accuracy: 0.9692, Val Loss: 0.1041, Val Accuracy: 0.9597 Epoch 746/1000, Train Loss: 0.0862, Train Accuracy: 0.9697, Val Loss: 0.1041, Val Accuracy: 0.9596 Epoch 747/1000, Train Loss: 0.0868, Train Accuracy: 0.9696, Val Loss: 0.1040, Val Accuracy: 0.9597 Epoch 748/1000, Train Loss: 0.0850, Train Accuracy: 0.9714, Val Loss: 0.1040, Val Accuracy: 0.9597 Epoch 749/1000, Train Loss: 0.0861, Train Accuracy: 0.9704, Val Loss: 0.1039, Val Accuracy: 0.9599 Epoch 750/1000, Train Loss: 0.0868, Train Accuracy: 0.9696, Val Loss: 0.1039, Val Accuracy: 0.9597 Epoch 751/1000, Train Loss: 0.0856, Train Accuracy: 0.9706, Val Loss: 0.1038, Val Accuracy: 0.9597 Epoch 752/1000, Train Loss: 0.0860, Train Accuracy: 0.9697, Val Loss: 0.1035, Val Accuracy: 0.9597 Epoch 753/1000, Train Loss: 0.0859, Train Accuracy: 0.9710, Val Loss: 0.1035, Val Accuracy: 0.9599 Epoch 754/1000, Train Loss: 0.0873, Train Accuracy: 0.9693, Val Loss: 0.1033, Val Accuracy: 0.9597 Epoch 755/1000, Train Loss: 0.0852, Train Accuracy: 0.9719, Val Loss: 0.1032, Val Accuracy: 0.9597 Epoch 756/1000, Train Loss: 0.0834, Train Accuracy: 0.9717, Val Loss: 0.1034, Val Accuracy: 0.9597 Epoch 757/1000, Train Loss: 0.0848, Train Accuracy: 0.9703, Val Loss: 0.1032, Val Accuracy: 0.9599 Epoch 758/1000, Train Loss: 0.0852, Train Accuracy: 0.9703, Val Loss: 0.1033, Val Accuracy: 0.9599 Epoch 759/1000, Train Loss: 0.0836, Train Accuracy: 0.9714, Val Loss: 0.1031, Val Accuracy: 0.9596 Epoch 760/1000, Train Loss: 0.0858, Train Accuracy: 0.9707, Val Loss: 0.1029, Val Accuracy: 0.9599 Epoch 761/1000, Train Loss: 0.0842, Train Accuracy: 0.9710, Val Loss: 0.1028, Val Accuracy: 0.9599 Epoch 762/1000, Train Loss: 0.0848, Train Accuracy: 0.9709, Val Loss: 0.1030, Val Accuracy: 0.9599 Epoch 763/1000, Train Loss: 0.0843, Train Accuracy: 0.9713, Val Loss: 0.1030, Val Accuracy: 0.9599 Epoch 764/1000, Train Loss: 0.0850, Train Accuracy: 0.9703, Val Loss: 0.1026, Val Accuracy: 0.9601 Epoch 765/1000, Train Loss: 0.0857, Train Accuracy: 0.9709, Val Loss: 0.1027, Val Accuracy: 0.9599 Epoch 766/1000, Train Loss: 0.0835, Train Accuracy: 0.9711, Val Loss: 0.1025, Val Accuracy: 0.9599 Epoch 767/1000, Train Loss: 0.0840, Train Accuracy: 0.9707, Val Loss: 0.1024, Val Accuracy: 0.9599 Epoch 768/1000, Train Loss: 0.0844, Train Accuracy: 0.9703, Val Loss: 0.1022, Val Accuracy: 0.9602 Epoch 769/1000, Train Loss: 0.0847, Train Accuracy: 0.9702, Val Loss: 0.1025, Val Accuracy: 0.9601 Epoch 770/1000, Train Loss: 0.0825, Train Accuracy: 0.9716, Val Loss: 0.1024, Val Accuracy: 0.9601 Epoch 771/1000, Train Loss: 0.0830, Train Accuracy: 0.9722, Val Loss: 0.1023, Val Accuracy: 0.9597 Epoch 772/1000, Train Loss: 0.0824, Train Accuracy: 0.9718, Val Loss: 0.1024, Val Accuracy: 0.9602 Epoch 773/1000, Train Loss: 0.0834, Train Accuracy: 0.9723, Val Loss: 0.1022, Val Accuracy: 0.9599 Epoch 774/1000, Train Loss: 0.0821, Train Accuracy: 0.9710, Val Loss: 0.1022, Val Accuracy: 0.9597 Epoch 775/1000, Train Loss: 0.0827, Train Accuracy: 0.9725, Val Loss: 0.1020, Val Accuracy: 0.9606 Epoch 776/1000, Train Loss: 0.0833, Train Accuracy: 0.9710, Val Loss: 0.1020, Val Accuracy: 0.9604 Epoch 777/1000, Train Loss: 0.0836, Train Accuracy: 0.9709, Val Loss: 0.1020, Val Accuracy: 0.9601 Epoch 778/1000, Train Loss: 0.0829, Train Accuracy: 0.9710, Val Loss: 0.1019, Val Accuracy: 0.9601 Epoch 779/1000, Train Loss: 0.0820, Train Accuracy: 0.9724, Val Loss: 0.1018, Val Accuracy: 0.9606 Epoch 780/1000, Train Loss: 0.0828, Train Accuracy: 0.9713, Val Loss: 0.1017, Val Accuracy: 0.9606 Epoch 781/1000, Train Loss: 0.0818, Train Accuracy: 0.9713, Val Loss: 0.1018, Val Accuracy: 0.9602 Epoch 782/1000, Train Loss: 0.0818, Train Accuracy: 0.9720, Val Loss: 0.1018, Val Accuracy: 0.9602 Epoch 783/1000, Train Loss: 0.0806, Train Accuracy: 0.9726, Val Loss: 0.1016, Val Accuracy: 0.9604 Epoch 784/1000, Train Loss: 0.0814, Train Accuracy: 0.9719, Val Loss: 0.1016, Val Accuracy: 0.9606 Epoch 785/1000, Train Loss: 0.0796, Train Accuracy: 0.9731, Val Loss: 0.1016, Val Accuracy: 0.9602 Epoch 786/1000, Train Loss: 0.0822, Train Accuracy: 0.9715, Val Loss: 0.1017, Val Accuracy: 0.9606 Epoch 787/1000, Train Loss: 0.0821, Train Accuracy: 0.9719, Val Loss: 0.1014, Val Accuracy: 0.9604 Epoch 788/1000, Train Loss: 0.0821, Train Accuracy: 0.9719, Val Loss: 0.1013, Val Accuracy: 0.9604 Epoch 789/1000, Train Loss: 0.0793, Train Accuracy: 0.9741, Val Loss: 0.1012, Val Accuracy: 0.9608 Epoch 790/1000, Train Loss: 0.0820, Train Accuracy: 0.9719, Val Loss: 0.1012, Val Accuracy: 0.9604 Epoch 791/1000, Train Loss: 0.0815, Train Accuracy: 0.9729, Val Loss: 0.1010, Val Accuracy: 0.9604 Epoch 792/1000, Train Loss: 0.0814, Train Accuracy: 0.9721, Val Loss: 0.1010, Val Accuracy: 0.9608 Epoch 793/1000, Train Loss: 0.0790, Train Accuracy: 0.9730, Val Loss: 0.1010, Val Accuracy: 0.9606 Epoch 794/1000, Train Loss: 0.0817, Train Accuracy: 0.9715, Val Loss: 0.1009, Val Accuracy: 0.9604 Epoch 795/1000, Train Loss: 0.0802, Train Accuracy: 0.9725, Val Loss: 0.1011, Val Accuracy: 0.9608 Epoch 796/1000, Train Loss: 0.0817, Train Accuracy: 0.9722, Val Loss: 0.1012, Val Accuracy: 0.9608 Epoch 797/1000, Train Loss: 0.0778, Train Accuracy: 0.9738, Val Loss: 0.1009, Val Accuracy: 0.9608 Epoch 798/1000, Train Loss: 0.0795, Train Accuracy: 0.9734, Val Loss: 0.1006, Val Accuracy: 0.9608 Epoch 799/1000, Train Loss: 0.0797, Train Accuracy: 0.9726, Val Loss: 0.1008, Val Accuracy: 0.9604 Epoch 800/1000, Train Loss: 0.0779, Train Accuracy: 0.9746, Val Loss: 0.1007, Val Accuracy: 0.9608 Epoch 801/1000, Train Loss: 0.0777, Train Accuracy: 0.9736, Val Loss: 0.1003, Val Accuracy: 0.9609 Epoch 802/1000, Train Loss: 0.0796, Train Accuracy: 0.9728, Val Loss: 0.1003, Val Accuracy: 0.9606 Epoch 803/1000, Train Loss: 0.0783, Train Accuracy: 0.9734, Val Loss: 0.1002, Val Accuracy: 0.9606 Epoch 804/1000, Train Loss: 0.0779, Train Accuracy: 0.9746, Val Loss: 0.1002, Val Accuracy: 0.9608 Epoch 805/1000, Train Loss: 0.0795, Train Accuracy: 0.9720, Val Loss: 0.1003, Val Accuracy: 0.9609 Epoch 806/1000, Train Loss: 0.0776, Train Accuracy: 0.9730, Val Loss: 0.1002, Val Accuracy: 0.9609 Epoch 807/1000, Train Loss: 0.0782, Train Accuracy: 0.9736, Val Loss: 0.1002, Val Accuracy: 0.9609 Epoch 808/1000, Train Loss: 0.0782, Train Accuracy: 0.9727, Val Loss: 0.1001, Val Accuracy: 0.9609 Epoch 809/1000, Train Loss: 0.0781, Train Accuracy: 0.9736, Val Loss: 0.1001, Val Accuracy: 0.9611 Epoch 810/1000, Train Loss: 0.0795, Train Accuracy: 0.9723, Val Loss: 0.0999, Val Accuracy: 0.9611 Epoch 811/1000, Train Loss: 0.0787, Train Accuracy: 0.9734, Val Loss: 0.1001, Val Accuracy: 0.9606 Epoch 812/1000, Train Loss: 0.0795, Train Accuracy: 0.9718, Val Loss: 0.0999, Val Accuracy: 0.9611 Epoch 813/1000, Train Loss: 0.0778, Train Accuracy: 0.9734, Val Loss: 0.1000, Val Accuracy: 0.9609 Epoch 814/1000, Train Loss: 0.0773, Train Accuracy: 0.9735, Val Loss: 0.0999, Val Accuracy: 0.9620 Epoch 815/1000, Train Loss: 0.0786, Train Accuracy: 0.9730, Val Loss: 0.0997, Val Accuracy: 0.9614 Epoch 816/1000, Train Loss: 0.0775, Train Accuracy: 0.9735, Val Loss: 0.0998, Val Accuracy: 0.9614 Epoch 817/1000, Train Loss: 0.0767, Train Accuracy: 0.9738, Val Loss: 0.0997, Val Accuracy: 0.9614 Epoch 818/1000, Train Loss: 0.0782, Train Accuracy: 0.9732, Val Loss: 0.0996, Val Accuracy: 0.9613 Epoch 819/1000, Train Loss: 0.0758, Train Accuracy: 0.9746, Val Loss: 0.0996, Val Accuracy: 0.9616 Epoch 820/1000, Train Loss: 0.0765, Train Accuracy: 0.9746, Val Loss: 0.0996, Val Accuracy: 0.9618 Epoch 821/1000, Train Loss: 0.0773, Train Accuracy: 0.9744, Val Loss: 0.0996, Val Accuracy: 0.9620 Epoch 822/1000, Train Loss: 0.0737, Train Accuracy: 0.9762, Val Loss: 0.0994, Val Accuracy: 0.9618 Epoch 823/1000, Train Loss: 0.0770, Train Accuracy: 0.9730, Val Loss: 0.0993, Val Accuracy: 0.9616 Epoch 824/1000, Train Loss: 0.0764, Train Accuracy: 0.9747, Val Loss: 0.0991, Val Accuracy: 0.9616 Epoch 825/1000, Train Loss: 0.0785, Train Accuracy: 0.9728, Val Loss: 0.0994, Val Accuracy: 0.9621 Epoch 826/1000, Train Loss: 0.0767, Train Accuracy: 0.9737, Val Loss: 0.0994, Val Accuracy: 0.9621 Epoch 827/1000, Train Loss: 0.0771, Train Accuracy: 0.9742, Val Loss: 0.0993, Val Accuracy: 0.9620 Epoch 828/1000, Train Loss: 0.0745, Train Accuracy: 0.9750, Val Loss: 0.0990, Val Accuracy: 0.9616 Epoch 829/1000, Train Loss: 0.0760, Train Accuracy: 0.9742, Val Loss: 0.0992, Val Accuracy: 0.9621 Epoch 830/1000, Train Loss: 0.0757, Train Accuracy: 0.9738, Val Loss: 0.0990, Val Accuracy: 0.9620 Epoch 831/1000, Train Loss: 0.0755, Train Accuracy: 0.9743, Val Loss: 0.0988, Val Accuracy: 0.9618 Epoch 832/1000, Train Loss: 0.0756, Train Accuracy: 0.9751, Val Loss: 0.0989, Val Accuracy: 0.9620 Epoch 833/1000, Train Loss: 0.0751, Train Accuracy: 0.9748, Val Loss: 0.0986, Val Accuracy: 0.9620 Epoch 834/1000, Train Loss: 0.0749, Train Accuracy: 0.9744, Val Loss: 0.0987, Val Accuracy: 0.9620 Epoch 835/1000, Train Loss: 0.0726, Train Accuracy: 0.9756, Val Loss: 0.0988, Val Accuracy: 0.9621 Epoch 836/1000, Train Loss: 0.0759, Train Accuracy: 0.9750, Val Loss: 0.0986, Val Accuracy: 0.9620 Epoch 837/1000, Train Loss: 0.0752, Train Accuracy: 0.9746, Val Loss: 0.0985, Val Accuracy: 0.9618 Epoch 838/1000, Train Loss: 0.0737, Train Accuracy: 0.9748, Val Loss: 0.0985, Val Accuracy: 0.9614 Epoch 839/1000, Train Loss: 0.0746, Train Accuracy: 0.9744, Val Loss: 0.0985, Val Accuracy: 0.9618 Epoch 840/1000, Train Loss: 0.0757, Train Accuracy: 0.9740, Val Loss: 0.0983, Val Accuracy: 0.9620 Epoch 841/1000, Train Loss: 0.0739, Train Accuracy: 0.9753, Val Loss: 0.0982, Val Accuracy: 0.9625 Epoch 842/1000, Train Loss: 0.0753, Train Accuracy: 0.9744, Val Loss: 0.0984, Val Accuracy: 0.9621 Epoch 843/1000, Train Loss: 0.0745, Train Accuracy: 0.9744, Val Loss: 0.0983, Val Accuracy: 0.9621 Epoch 844/1000, Train Loss: 0.0733, Train Accuracy: 0.9756, Val Loss: 0.0985, Val Accuracy: 0.9623 Epoch 845/1000, Train Loss: 0.0741, Train Accuracy: 0.9741, Val Loss: 0.0978, Val Accuracy: 0.9628 Epoch 846/1000, Train Loss: 0.0740, Train Accuracy: 0.9749, Val Loss: 0.0979, Val Accuracy: 0.9627 Epoch 847/1000, Train Loss: 0.0722, Train Accuracy: 0.9751, Val Loss: 0.0979, Val Accuracy: 0.9625 Epoch 848/1000, Train Loss: 0.0742, Train Accuracy: 0.9751, Val Loss: 0.0980, Val Accuracy: 0.9623 Epoch 849/1000, Train Loss: 0.0737, Train Accuracy: 0.9749, Val Loss: 0.0980, Val Accuracy: 0.9623 Epoch 850/1000, Train Loss: 0.0732, Train Accuracy: 0.9744, Val Loss: 0.0979, Val Accuracy: 0.9625 Epoch 851/1000, Train Loss: 0.0740, Train Accuracy: 0.9748, Val Loss: 0.0982, Val Accuracy: 0.9620 Epoch 852/1000, Train Loss: 0.0719, Train Accuracy: 0.9757, Val Loss: 0.0978, Val Accuracy: 0.9623 Epoch 853/1000, Train Loss: 0.0724, Train Accuracy: 0.9752, Val Loss: 0.0977, Val Accuracy: 0.9627 Epoch 854/1000, Train Loss: 0.0726, Train Accuracy: 0.9752, Val Loss: 0.0978, Val Accuracy: 0.9623 Epoch 855/1000, Train Loss: 0.0693, Train Accuracy: 0.9774, Val Loss: 0.0974, Val Accuracy: 0.9628 Epoch 856/1000, Train Loss: 0.0727, Train Accuracy: 0.9750, Val Loss: 0.0977, Val Accuracy: 0.9630 Epoch 857/1000, Train Loss: 0.0728, Train Accuracy: 0.9752, Val Loss: 0.0976, Val Accuracy: 0.9628 Epoch 858/1000, Train Loss: 0.0731, Train Accuracy: 0.9759, Val Loss: 0.0974, Val Accuracy: 0.9628 Epoch 859/1000, Train Loss: 0.0725, Train Accuracy: 0.9754, Val Loss: 0.0974, Val Accuracy: 0.9630 Epoch 860/1000, Train Loss: 0.0711, Train Accuracy: 0.9769, Val Loss: 0.0972, Val Accuracy: 0.9632 Epoch 861/1000, Train Loss: 0.0725, Train Accuracy: 0.9755, Val Loss: 0.0973, Val Accuracy: 0.9633 Epoch 862/1000, Train Loss: 0.0702, Train Accuracy: 0.9759, Val Loss: 0.0973, Val Accuracy: 0.9632 Epoch 863/1000, Train Loss: 0.0709, Train Accuracy: 0.9759, Val Loss: 0.0972, Val Accuracy: 0.9632 Epoch 864/1000, Train Loss: 0.0703, Train Accuracy: 0.9767, Val Loss: 0.0972, Val Accuracy: 0.9630 Epoch 865/1000, Train Loss: 0.0705, Train Accuracy: 0.9765, Val Loss: 0.0973, Val Accuracy: 0.9632 Epoch 866/1000, Train Loss: 0.0723, Train Accuracy: 0.9747, Val Loss: 0.0973, Val Accuracy: 0.9633 Epoch 867/1000, Train Loss: 0.0712, Train Accuracy: 0.9756, Val Loss: 0.0969, Val Accuracy: 0.9635 Epoch 868/1000, Train Loss: 0.0691, Train Accuracy: 0.9768, Val Loss: 0.0968, Val Accuracy: 0.9635 Epoch 869/1000, Train Loss: 0.0700, Train Accuracy: 0.9770, Val Loss: 0.0973, Val Accuracy: 0.9633 Epoch 870/1000, Train Loss: 0.0718, Train Accuracy: 0.9764, Val Loss: 0.0969, Val Accuracy: 0.9637 Epoch 871/1000, Train Loss: 0.0704, Train Accuracy: 0.9767, Val Loss: 0.0965, Val Accuracy: 0.9637 Epoch 872/1000, Train Loss: 0.0721, Train Accuracy: 0.9759, Val Loss: 0.0967, Val Accuracy: 0.9633 Epoch 873/1000, Train Loss: 0.0721, Train Accuracy: 0.9762, Val Loss: 0.0967, Val Accuracy: 0.9635 Epoch 874/1000, Train Loss: 0.0718, Train Accuracy: 0.9762, Val Loss: 0.0965, Val Accuracy: 0.9633 Epoch 875/1000, Train Loss: 0.0698, Train Accuracy: 0.9765, Val Loss: 0.0970, Val Accuracy: 0.9639 Epoch 876/1000, Train Loss: 0.0688, Train Accuracy: 0.9771, Val Loss: 0.0965, Val Accuracy: 0.9633 Epoch 877/1000, Train Loss: 0.0686, Train Accuracy: 0.9781, Val Loss: 0.0967, Val Accuracy: 0.9640 Epoch 878/1000, Train Loss: 0.0714, Train Accuracy: 0.9763, Val Loss: 0.0965, Val Accuracy: 0.9637 Epoch 879/1000, Train Loss: 0.0710, Train Accuracy: 0.9759, Val Loss: 0.0965, Val Accuracy: 0.9635 Epoch 880/1000, Train Loss: 0.0685, Train Accuracy: 0.9774, Val Loss: 0.0963, Val Accuracy: 0.9637 Epoch 881/1000, Train Loss: 0.0681, Train Accuracy: 0.9769, Val Loss: 0.0964, Val Accuracy: 0.9633 Epoch 882/1000, Train Loss: 0.0686, Train Accuracy: 0.9764, Val Loss: 0.0961, Val Accuracy: 0.9637 Epoch 883/1000, Train Loss: 0.0708, Train Accuracy: 0.9762, Val Loss: 0.0961, Val Accuracy: 0.9639 Epoch 884/1000, Train Loss: 0.0702, Train Accuracy: 0.9763, Val Loss: 0.0961, Val Accuracy: 0.9635 Epoch 885/1000, Train Loss: 0.0687, Train Accuracy: 0.9759, Val Loss: 0.0964, Val Accuracy: 0.9639 Epoch 886/1000, Train Loss: 0.0710, Train Accuracy: 0.9756, Val Loss: 0.0959, Val Accuracy: 0.9642 Epoch 887/1000, Train Loss: 0.0692, Train Accuracy: 0.9769, Val Loss: 0.0962, Val Accuracy: 0.9639 Epoch 888/1000, Train Loss: 0.0668, Train Accuracy: 0.9781, Val Loss: 0.0959, Val Accuracy: 0.9639 Epoch 889/1000, Train Loss: 0.0679, Train Accuracy: 0.9772, Val Loss: 0.0961, Val Accuracy: 0.9637 Epoch 890/1000, Train Loss: 0.0674, Train Accuracy: 0.9779, Val Loss: 0.0962, Val Accuracy: 0.9640 Epoch 891/1000, Train Loss: 0.0678, Train Accuracy: 0.9767, Val Loss: 0.0961, Val Accuracy: 0.9644 Epoch 892/1000, Train Loss: 0.0685, Train Accuracy: 0.9777, Val Loss: 0.0959, Val Accuracy: 0.9640 Epoch 893/1000, Train Loss: 0.0683, Train Accuracy: 0.9771, Val Loss: 0.0958, Val Accuracy: 0.9640 Epoch 894/1000, Train Loss: 0.0690, Train Accuracy: 0.9781, Val Loss: 0.0956, Val Accuracy: 0.9640 Epoch 895/1000, Train Loss: 0.0673, Train Accuracy: 0.9781, Val Loss: 0.0956, Val Accuracy: 0.9639 Epoch 896/1000, Train Loss: 0.0695, Train Accuracy: 0.9768, Val Loss: 0.0960, Val Accuracy: 0.9642 Epoch 897/1000, Train Loss: 0.0678, Train Accuracy: 0.9766, Val Loss: 0.0957, Val Accuracy: 0.9642 Epoch 898/1000, Train Loss: 0.0668, Train Accuracy: 0.9773, Val Loss: 0.0956, Val Accuracy: 0.9642 Epoch 899/1000, Train Loss: 0.0662, Train Accuracy: 0.9782, Val Loss: 0.0958, Val Accuracy: 0.9645 Epoch 900/1000, Train Loss: 0.0662, Train Accuracy: 0.9779, Val Loss: 0.0955, Val Accuracy: 0.9642 Epoch 901/1000, Train Loss: 0.0668, Train Accuracy: 0.9769, Val Loss: 0.0953, Val Accuracy: 0.9642 Epoch 902/1000, Train Loss: 0.0686, Train Accuracy: 0.9770, Val Loss: 0.0953, Val Accuracy: 0.9642 Epoch 903/1000, Train Loss: 0.0657, Train Accuracy: 0.9784, Val Loss: 0.0956, Val Accuracy: 0.9642 Epoch 904/1000, Train Loss: 0.0674, Train Accuracy: 0.9769, Val Loss: 0.0952, Val Accuracy: 0.9645 Epoch 905/1000, Train Loss: 0.0674, Train Accuracy: 0.9780, Val Loss: 0.0950, Val Accuracy: 0.9642 Epoch 906/1000, Train Loss: 0.0650, Train Accuracy: 0.9787, Val Loss: 0.0955, Val Accuracy: 0.9644 Epoch 907/1000, Train Loss: 0.0649, Train Accuracy: 0.9792, Val Loss: 0.0953, Val Accuracy: 0.9644 Epoch 908/1000, Train Loss: 0.0671, Train Accuracy: 0.9775, Val Loss: 0.0951, Val Accuracy: 0.9644 Epoch 909/1000, Train Loss: 0.0659, Train Accuracy: 0.9782, Val Loss: 0.0950, Val Accuracy: 0.9642 Epoch 910/1000, Train Loss: 0.0671, Train Accuracy: 0.9784, Val Loss: 0.0951, Val Accuracy: 0.9644 Epoch 911/1000, Train Loss: 0.0667, Train Accuracy: 0.9782, Val Loss: 0.0951, Val Accuracy: 0.9640 Epoch 912/1000, Train Loss: 0.0653, Train Accuracy: 0.9782, Val Loss: 0.0949, Val Accuracy: 0.9640 Epoch 913/1000, Train Loss: 0.0653, Train Accuracy: 0.9781, Val Loss: 0.0952, Val Accuracy: 0.9647 Epoch 914/1000, Train Loss: 0.0654, Train Accuracy: 0.9778, Val Loss: 0.0953, Val Accuracy: 0.9645 Epoch 915/1000, Train Loss: 0.0643, Train Accuracy: 0.9790, Val Loss: 0.0952, Val Accuracy: 0.9645 Epoch 916/1000, Train Loss: 0.0645, Train Accuracy: 0.9781, Val Loss: 0.0949, Val Accuracy: 0.9645 Epoch 917/1000, Train Loss: 0.0651, Train Accuracy: 0.9796, Val Loss: 0.0950, Val Accuracy: 0.9645 Epoch 918/1000, Train Loss: 0.0652, Train Accuracy: 0.9776, Val Loss: 0.0946, Val Accuracy: 0.9644 Epoch 919/1000, Train Loss: 0.0660, Train Accuracy: 0.9781, Val Loss: 0.0947, Val Accuracy: 0.9642 Epoch 920/1000, Train Loss: 0.0648, Train Accuracy: 0.9789, Val Loss: 0.0948, Val Accuracy: 0.9644 Epoch 921/1000, Train Loss: 0.0644, Train Accuracy: 0.9776, Val Loss: 0.0947, Val Accuracy: 0.9647 Epoch 922/1000, Train Loss: 0.0636, Train Accuracy: 0.9802, Val Loss: 0.0949, Val Accuracy: 0.9647 Epoch 923/1000, Train Loss: 0.0662, Train Accuracy: 0.9775, Val Loss: 0.0944, Val Accuracy: 0.9647 Epoch 924/1000, Train Loss: 0.0651, Train Accuracy: 0.9781, Val Loss: 0.0946, Val Accuracy: 0.9649 Epoch 925/1000, Train Loss: 0.0651, Train Accuracy: 0.9787, Val Loss: 0.0945, Val Accuracy: 0.9649 Epoch 926/1000, Train Loss: 0.0660, Train Accuracy: 0.9786, Val Loss: 0.0945, Val Accuracy: 0.9647 Epoch 927/1000, Train Loss: 0.0627, Train Accuracy: 0.9790, Val Loss: 0.0943, Val Accuracy: 0.9647 Epoch 928/1000, Train Loss: 0.0628, Train Accuracy: 0.9798, Val Loss: 0.0943, Val Accuracy: 0.9644 Epoch 929/1000, Train Loss: 0.0629, Train Accuracy: 0.9789, Val Loss: 0.0943, Val Accuracy: 0.9647 Epoch 930/1000, Train Loss: 0.0637, Train Accuracy: 0.9793, Val Loss: 0.0941, Val Accuracy: 0.9645 Epoch 931/1000, Train Loss: 0.0633, Train Accuracy: 0.9788, Val Loss: 0.0940, Val Accuracy: 0.9649 Epoch 932/1000, Train Loss: 0.0649, Train Accuracy: 0.9787, Val Loss: 0.0943, Val Accuracy: 0.9647 Epoch 933/1000, Train Loss: 0.0636, Train Accuracy: 0.9788, Val Loss: 0.0940, Val Accuracy: 0.9651 Epoch 934/1000, Train Loss: 0.0631, Train Accuracy: 0.9793, Val Loss: 0.0944, Val Accuracy: 0.9649 Epoch 935/1000, Train Loss: 0.0633, Train Accuracy: 0.9795, Val Loss: 0.0941, Val Accuracy: 0.9649 Epoch 936/1000, Train Loss: 0.0634, Train Accuracy: 0.9787, Val Loss: 0.0939, Val Accuracy: 0.9645 Epoch 937/1000, Train Loss: 0.0631, Train Accuracy: 0.9787, Val Loss: 0.0943, Val Accuracy: 0.9651 Epoch 938/1000, Train Loss: 0.0633, Train Accuracy: 0.9785, Val Loss: 0.0939, Val Accuracy: 0.9649 Epoch 939/1000, Train Loss: 0.0639, Train Accuracy: 0.9788, Val Loss: 0.0939, Val Accuracy: 0.9649 Epoch 940/1000, Train Loss: 0.0624, Train Accuracy: 0.9795, Val Loss: 0.0938, Val Accuracy: 0.9651 Epoch 941/1000, Train Loss: 0.0629, Train Accuracy: 0.9793, Val Loss: 0.0945, Val Accuracy: 0.9649 Epoch 942/1000, Train Loss: 0.0620, Train Accuracy: 0.9787, Val Loss: 0.0939, Val Accuracy: 0.9652 Epoch 943/1000, Train Loss: 0.0629, Train Accuracy: 0.9795, Val Loss: 0.0939, Val Accuracy: 0.9649 Epoch 944/1000, Train Loss: 0.0626, Train Accuracy: 0.9797, Val Loss: 0.0937, Val Accuracy: 0.9651 Epoch 945/1000, Train Loss: 0.0625, Train Accuracy: 0.9790, Val Loss: 0.0937, Val Accuracy: 0.9647 Epoch 946/1000, Train Loss: 0.0639, Train Accuracy: 0.9786, Val Loss: 0.0939, Val Accuracy: 0.9649 Epoch 947/1000, Train Loss: 0.0613, Train Accuracy: 0.9807, Val Loss: 0.0936, Val Accuracy: 0.9644 Epoch 948/1000, Train Loss: 0.0626, Train Accuracy: 0.9789, Val Loss: 0.0937, Val Accuracy: 0.9651 Epoch 949/1000, Train Loss: 0.0614, Train Accuracy: 0.9799, Val Loss: 0.0938, Val Accuracy: 0.9649 Epoch 950/1000, Train Loss: 0.0609, Train Accuracy: 0.9804, Val Loss: 0.0937, Val Accuracy: 0.9651 Epoch 951/1000, Train Loss: 0.0607, Train Accuracy: 0.9807, Val Loss: 0.0934, Val Accuracy: 0.9651 Epoch 952/1000, Train Loss: 0.0611, Train Accuracy: 0.9803, Val Loss: 0.0932, Val Accuracy: 0.9645 Epoch 953/1000, Train Loss: 0.0608, Train Accuracy: 0.9802, Val Loss: 0.0934, Val Accuracy: 0.9652 Epoch 954/1000, Train Loss: 0.0606, Train Accuracy: 0.9799, Val Loss: 0.0933, Val Accuracy: 0.9651 Epoch 955/1000, Train Loss: 0.0609, Train Accuracy: 0.9804, Val Loss: 0.0931, Val Accuracy: 0.9651 Epoch 956/1000, Train Loss: 0.0609, Train Accuracy: 0.9801, Val Loss: 0.0934, Val Accuracy: 0.9652 Epoch 957/1000, Train Loss: 0.0604, Train Accuracy: 0.9798, Val Loss: 0.0933, Val Accuracy: 0.9652 Epoch 958/1000, Train Loss: 0.0614, Train Accuracy: 0.9801, Val Loss: 0.0933, Val Accuracy: 0.9652 Epoch 959/1000, Train Loss: 0.0606, Train Accuracy: 0.9793, Val Loss: 0.0929, Val Accuracy: 0.9649 Epoch 960/1000, Train Loss: 0.0599, Train Accuracy: 0.9804, Val Loss: 0.0932, Val Accuracy: 0.9652 Epoch 961/1000, Train Loss: 0.0612, Train Accuracy: 0.9797, Val Loss: 0.0932, Val Accuracy: 0.9652 Epoch 962/1000, Train Loss: 0.0607, Train Accuracy: 0.9804, Val Loss: 0.0931, Val Accuracy: 0.9651 Epoch 963/1000, Train Loss: 0.0618, Train Accuracy: 0.9793, Val Loss: 0.0927, Val Accuracy: 0.9656 Epoch 964/1000, Train Loss: 0.0597, Train Accuracy: 0.9809, Val Loss: 0.0933, Val Accuracy: 0.9656 Epoch 965/1000, Train Loss: 0.0606, Train Accuracy: 0.9797, Val Loss: 0.0930, Val Accuracy: 0.9654 Epoch 966/1000, Train Loss: 0.0583, Train Accuracy: 0.9804, Val Loss: 0.0928, Val Accuracy: 0.9652 Epoch 967/1000, Train Loss: 0.0618, Train Accuracy: 0.9790, Val Loss: 0.0930, Val Accuracy: 0.9651 Epoch 968/1000, Train Loss: 0.0590, Train Accuracy: 0.9809, Val Loss: 0.0925, Val Accuracy: 0.9647 Epoch 969/1000, Train Loss: 0.0586, Train Accuracy: 0.9813, Val Loss: 0.0927, Val Accuracy: 0.9651 Epoch 970/1000, Train Loss: 0.0608, Train Accuracy: 0.9806, Val Loss: 0.0927, Val Accuracy: 0.9649 Epoch 971/1000, Train Loss: 0.0580, Train Accuracy: 0.9803, Val Loss: 0.0931, Val Accuracy: 0.9657 Epoch 972/1000, Train Loss: 0.0591, Train Accuracy: 0.9809, Val Loss: 0.0926, Val Accuracy: 0.9654 Epoch 973/1000, Train Loss: 0.0599, Train Accuracy: 0.9802, Val Loss: 0.0933, Val Accuracy: 0.9652 Epoch 974/1000, Train Loss: 0.0594, Train Accuracy: 0.9796, Val Loss: 0.0929, Val Accuracy: 0.9659 Epoch 975/1000, Train Loss: 0.0594, Train Accuracy: 0.9805, Val Loss: 0.0926, Val Accuracy: 0.9656 Epoch 976/1000, Train Loss: 0.0595, Train Accuracy: 0.9811, Val Loss: 0.0925, Val Accuracy: 0.9659 Epoch 977/1000, Train Loss: 0.0592, Train Accuracy: 0.9811, Val Loss: 0.0923, Val Accuracy: 0.9657 Epoch 978/1000, Train Loss: 0.0596, Train Accuracy: 0.9802, Val Loss: 0.0922, Val Accuracy: 0.9654 Epoch 979/1000, Train Loss: 0.0574, Train Accuracy: 0.9812, Val Loss: 0.0928, Val Accuracy: 0.9659 Epoch 980/1000, Train Loss: 0.0578, Train Accuracy: 0.9812, Val Loss: 0.0925, Val Accuracy: 0.9657 Epoch 981/1000, Train Loss: 0.0588, Train Accuracy: 0.9808, Val Loss: 0.0924, Val Accuracy: 0.9659 Epoch 982/1000, Train Loss: 0.0585, Train Accuracy: 0.9803, Val Loss: 0.0929, Val Accuracy: 0.9656 Epoch 983/1000, Train Loss: 0.0578, Train Accuracy: 0.9813, Val Loss: 0.0922, Val Accuracy: 0.9657 Epoch 984/1000, Train Loss: 0.0577, Train Accuracy: 0.9816, Val Loss: 0.0927, Val Accuracy: 0.9657 Epoch 985/1000, Train Loss: 0.0582, Train Accuracy: 0.9808, Val Loss: 0.0923, Val Accuracy: 0.9659 Epoch 986/1000, Train Loss: 0.0593, Train Accuracy: 0.9810, Val Loss: 0.0924, Val Accuracy: 0.9657 Epoch 987/1000, Train Loss: 0.0590, Train Accuracy: 0.9810, Val Loss: 0.0921, Val Accuracy: 0.9659 Epoch 988/1000, Train Loss: 0.0578, Train Accuracy: 0.9811, Val Loss: 0.0923, Val Accuracy: 0.9656 Epoch 989/1000, Train Loss: 0.0572, Train Accuracy: 0.9811, Val Loss: 0.0923, Val Accuracy: 0.9659 Epoch 990/1000, Train Loss: 0.0574, Train Accuracy: 0.9816, Val Loss: 0.0919, Val Accuracy: 0.9657 Epoch 991/1000, Train Loss: 0.0570, Train Accuracy: 0.9818, Val Loss: 0.0923, Val Accuracy: 0.9654 Epoch 992/1000, Train Loss: 0.0577, Train Accuracy: 0.9817, Val Loss: 0.0922, Val Accuracy: 0.9654 Epoch 993/1000, Train Loss: 0.0574, Train Accuracy: 0.9815, Val Loss: 0.0922, Val Accuracy: 0.9659 Epoch 994/1000, Train Loss: 0.0590, Train Accuracy: 0.9804, Val Loss: 0.0923, Val Accuracy: 0.9659 Epoch 995/1000, Train Loss: 0.0568, Train Accuracy: 0.9810, Val Loss: 0.0923, Val Accuracy: 0.9659 Epoch 996/1000, Train Loss: 0.0579, Train Accuracy: 0.9811, Val Loss: 0.0919, Val Accuracy: 0.9664 Epoch 997/1000, Train Loss: 0.0565, Train Accuracy: 0.9818, Val Loss: 0.0922, Val Accuracy: 0.9663 Epoch 998/1000, Train Loss: 0.0563, Train Accuracy: 0.9821, Val Loss: 0.0923, Val Accuracy: 0.9663 Epoch 999/1000, Train Loss: 0.0575, Train Accuracy: 0.9815, Val Loss: 0.0920, Val Accuracy: 0.9666 Epoch 1000/1000, Train Loss: 0.0565, Train Accuracy: 0.9820, Val Loss: 0.0919, Val Accuracy: 0.9666
# Create the neural network classifier
clf = MLPClassifier(random_state=42)
# Assuming you have performed a grid search and obtained the best estimator
# Define the parameter grid
param_grid = {
'hidden_layer_sizes': [(30,30), (10,10,10)],
'solver': ['adam', 'sgd'],
'alpha': [0.001, 0.01],
}
# Create the GridSearchCV object
grid_search = GridSearchCV(clf, param_grid, scoring='accuracy', cv=2)
# Fit the GridSearchCV object to the data
grid_search.fit(X_train_merged_nn, y_train_merged_nn)
# Save the best estimator in best_nn
best_nn_DS_merged = grid_search.best_estimator_
print(best_nn_DS_merged)
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn(
MLPClassifier(alpha=0.001, hidden_layer_sizes=(30, 30), random_state=42,
solver='sgd')
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn(
torch.save(model_merged_DS.state_dict(), best_model_path_merged)
¶
Test for the model dropseq dataset merged
This below is the step for testing our neural network with the merged dataset.
As before the model is set to evaluation mode, and test data is converted to PyTorch tensors.
A forward pass is performed on the test data to obtain predictions. The test loss is calculated using the same loss function as before and predictions are converted to binary values by applying a sigmoid function followed by rounding. Finally, The number of correct predictions is counted and used to compute the test accuracy.
# Load the best model
model_merged_DS.load_state_dict(torch.load('best_model_final_merged.pth'))
# Evaluate on test data
model_merged_DS.eval()
X_test_tensor_merged = torch.tensor(X_test_merged, dtype=torch.float32)
y_test_tensor_merged = torch.tensor(y_test_merged, dtype=torch.float32).unsqueeze(1)
with torch.no_grad():
test_output = model_merged_DS(X_test_tensor_merged)
test_loss = criterion(test_output, y_test_tensor_merged)
test_predictions = torch.round(torch.sigmoid(test_output))
test_correct = (test_predictions == y_test_tensor_merged).sum().item()
test_total = y_test_tensor_merged.size(0)
test_acc = test_correct / test_total
print(f'Test Loss: {test_loss.item():.4f}, Test Accuracy: {test_acc:.4f}')
Test Loss: 0.0930, Test Accuracy: 0.9652
¶
4.6) PREDICTORS ON ALL DATASET MERGED
We then decided to try to build a unique big classifier that is able to predict the target of all the four different datasets.
In order to do it, we needed to ensure that all the normalizations were the same, so we applied the same method to all the files to be consistent and to create a big dataset that is consistent with the noralization for all of its components.
To make the values remain non negativ, as having negative entries would not make any sense for our dataset sinc any entry represents how many times the correspondent gene is found in the RNA/DNA extracted from the cell, we used the MinMaxScaler.
We used it as it preserves the shape of the original distribution of the data and it ensures that all features are on the same scale, preventing features with larger ranges from dominating those with smaller ranges during model training.
We have then substituted the NaN values (generated from the fact that there were more than 8000 different genes) with 0 values, as in our case makes sense, since it represents the lack of that specific gene.
The division in training and test data than remains the same.
#i want to join the 4 datasets into a bigger one to try to build a general model
#in order to be consistent i need to perform a rescaling of the data for all the 4 datasets
#i will use the MinMaxScaler for that
scaler = MinMaxScaler()
df_SS_MCF7_norm_filtT.iloc[ : , :-1] = scaler.fit_transform(df_SS_MCF7_norm_filtT.iloc[ : , :-1])
df_SS_HCC1806_norm_filtT.iloc[ : , :-1] = scaler.fit_transform(df_SS_HCC1806_norm_filtT.iloc[ : , :-1])
df_DS_HCC1806_norm_filtT.iloc[ : , :-1] = scaler.fit_transform(df_DS_HCC1806_norm_filtT.iloc[ : , :-1])
df_DS_MCF7_norm_filtT.iloc[ : , :-1] = scaler.fit_transform(df_DS_MCF7_norm_filtT.iloc[ : , :-1])
#now i can join the dataframes
df_all = pd.concat([df_SS_MCF7_norm_filtT, df_SS_HCC1806_norm_filtT, df_DS_HCC1806_norm_filtT, df_DS_MCF7_norm_filtT])
#i replace the NaN values with 0
df_all.fillna(0, inplace=True)
df_all['target'] = np.where(df_all.index.str.contains('Hypo'), 1, 0)
df_all
#now i split the data into training and testing data, with a balance of 70% training and 30% testing
X_all = df_all.drop('target', axis=1)
y_all = df_all['target']
X_train_all, X_test_all, y_train_all, y_test_all = train_test_split(X_all, y_all, test_size=0.3, random_state=42)
¶
4.6.1) SVM
We used same approach used for SmartSeq MCF7, see 4.1.1.
# build SVM on ENTIRE DATASET min
# Define the SVM classifier
svm = SVC(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'kernel': ['linear', 'rbf']}
grid_search_svm = GridSearchCV(svm, param_grid, cv=2)
#outer_cv = KFold(n_splits=2, shuffle=True, random_state=42)
# Perform the cross validation
scores_svm = cross_val_score(grid_search_svm, X_train_all, y_train_all, cv=2)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores_svm))
Mean accuracy of nested cross validation: 0.9440080877206626
grid_search_svm.fit(X_train_all, y_train_all)
GridSearchCV(cv=2, estimator=SVC(random_state=42),
param_grid={'kernel': ['linear', 'rbf']})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=2, estimator=SVC(random_state=42),
param_grid={'kernel': ['linear', 'rbf']})SVC(random_state=42)
SVC(random_state=42)
# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
print("Best kernel: ", grid_search_svm.best_params_)
print("Best accuracy: ", grid_search_svm.best_score_)
best_svm_all = grid_search_svm.best_estimator_
print("Best model: ", best_svm_all)
# Test the model on the test dataset
print("Test accuracy: ", best_svm_all.score(X_test_all, y_test_all))
Best kernel: {'kernel': 'rbf'}
Best accuracy: 0.9440080877206626
Best model: SVC(random_state=42)
Test accuracy: 0.9543639992741789
¶
4.6.2) Random Forest
Here we find the building of the random forest classifier, following exactly the same passages of all the other ones.
The performance is quite good, it achieves a 94,81%, that we considered good as the joint dataset had huge dimensions.
Also in this most important feature ranking we can notice the presence of the genes BCYRN1, MT-RNR2 amd MALAT1.
#i build the random forest classifier for the general dataset
# Define the random forest classifier
rf = RandomForestClassifier(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [30, 50, 70], 'max_depth': [10, 20, 30]}
grid_search = GridSearchCV(rf, param_grid, cv=5)
outer_cv = KFold(n_splits=2, shuffle=True, random_state=42)
# Perform the cross validation
scores = cross_val_score(grid_search, X_train_all, y_train_all, cv=outer_cv)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))
# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
grid_search.fit(X_train_all, y_train_all)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)
#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_all, y_test_all))
# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_all.shape[1]):
print("%d. feature %s (%f)" % (f + 1, X_train_all.columns[indices[f]], importances[indices[f]]))
best_rf_all = grid_search.best_estimator_
Mean accuracy of nested cross validation: 0.9400419939342095
Best hyperparameters: {'max_depth': 30, 'n_estimators': 70}
Best accuracy: 0.9434637032503375
Best model: RandomForestClassifier(max_depth=30, n_estimators=70, random_state=42)
Test accuracy: 0.9481037924151696
Feature ranking:
1. feature "KRT19" (0.021781)
2. feature "TMSB10" (0.018618)
3. feature "TFF1" (0.017516)
4. feature "GAPDH" (0.015135)
5. feature "BCYRN1" (0.015076)
6. feature "MT-RNR2" (0.014077)
7. feature "PGK1" (0.013782)
8. feature "NDRG1" (0.013346)
9. feature "LGALS1" (0.013215)
10. feature "ENO1" (0.012655)
11. feature "FTL" (0.011469)
12. feature "TPI1" (0.009811)
13. feature "S100A10" (0.009186)
14. feature "RPS19" (0.009088)
15. feature "MALAT1" (0.008743)
16. feature "IGFBP3" (0.008361)
17. feature "MT-RNR1" (0.007893)
18. feature "RPS5" (0.007762)
19. feature "LDHA" (0.007553)
20. feature "ACTB" (0.007388)
21. feature "RPL35" (0.007065)
22. feature "RPS14" (0.006883)
23. feature "RPL13" (0.006779)
24. feature "RPL39" (0.006470)
25. feature "RPL41" (0.006367)
26. feature "S100A11" (0.006233)
27. feature "SERF2" (0.006110)
28. feature "TOB1" (0.006064)
29. feature "RPS28" (0.005967)
30. feature "TMEM258" (0.005753)
31. feature "SET" (0.005742)
32. feature "RPLP1" (0.005571)
33. feature "PKM" (0.005370)
34. feature "NCL" (0.005036)
35. feature "BNIP3" (0.005026)
36. feature "AKR1C1" (0.004855)
37. feature "HSPD1" (0.004844)
38. feature "HSP90B1" (0.004661)
39. feature "MT2A" (0.004595)
40. feature "P4HA1" (0.004579)
41. feature "AKR1C2" (0.004575)
42. feature "RPLP2" (0.004554)
43. feature "H4C3" (0.004549)
44. feature "HSP90AA1" (0.004503)
45. feature "ROMO1" (0.004473)
46. feature "HES1" (0.004470)
47. feature "RPS27" (0.004412)
48. feature "RPL35A" (0.004331)
49. feature "RPL34" (0.004227)
50. feature "RPL12" (0.004184)
51. feature "WDR43" (0.004164)
52. feature "FGF23" (0.003958)
53. feature "RPL15" (0.003924)
54. feature "DSP" (0.003894)
55. feature "MT-CYB" (0.003830)
56. feature "HSP90AB1" (0.003826)
57. feature "MT-CO3" (0.003818)
58. feature "RPL36" (0.003816)
59. feature "RPS15" (0.003744)
60. feature "RPS15A" (0.003528)
61. feature "GPM6A" (0.003433)
62. feature "TMEM64" (0.003413)
63. feature "B4GALT1" (0.003406)
64. feature "TFF3" (0.003353)
65. feature "RPS16" (0.003268)
66. feature "UBA52" (0.003217)
67. feature "MT-ND3" (0.003204)
68. feature "SNRPD2" (0.003188)
69. feature "RPL8" (0.003158)
70. feature "RPL30" (0.003118)
71. feature "RPS12" (0.003115)
72. feature "CALM2" (0.002932)
73. feature "HSPB1" (0.002920)
74. feature "MT-ATP6" (0.002912)
75. feature "S100A6" (0.002869)
76. feature "MT-ND4" (0.002850)
77. feature "MT-ND4L" (0.002806)
78. feature "BCAS3" (0.002725)
79. feature "KRT18" (0.002721)
80. feature "PKIB" (0.002708)
81. feature "PARD6B" (0.002659)
82. feature "MT-CO2" (0.002624)
83. feature "EIF3J" (0.002553)
84. feature "HNRNPAB" (0.002536)
85. feature "YWHAZ" (0.002515)
86. feature "ELOB" (0.002476)
87. feature "CSDE1" (0.002442)
88. feature "RPL23" (0.002376)
89. feature "RPL37A" (0.002368)
90. feature "KRT8" (0.002308)
91. feature "DNAJA1" (0.002293)
92. feature "RPS2" (0.002206)
93. feature "TPD52L1" (0.002140)
94. feature "NPM1P40" (0.002121)
95. feature "PSMA7" (0.002096)
96. feature "RPL37" (0.002096)
97. feature "BNIP3L" (0.002091)
98. feature "UQCRQ" (0.002056)
99. feature "AHNAK2" (0.002035)
100. feature "CAV1" (0.002025)
101. feature "COX7A2" (0.002007)
102. feature "MARCKS" (0.001989)
103. feature "PRRC2C" (0.001973)
104. feature "TRIM37" (0.001959)
105. feature "CAST" (0.001906)
106. feature "MT-ND5" (0.001895)
107. feature "TPT1" (0.001887)
108. feature "SOX4" (0.001853)
109. feature "PTMS" (0.001852)
110. feature "YBX1" (0.001852)
111. feature "YWHAB" (0.001850)
112. feature "RPL27A" (0.001840)
113. feature "EIF4G2" (0.001837)
114. feature "HNRNPU" (0.001794)
115. feature "ZC3H15" (0.001769)
116. feature "HNRNPA2B1" (0.001735)
117. feature "GPI" (0.001724)
118. feature "NCOA3" (0.001710)
119. feature "CBX3" (0.001709)
120. feature "RPL10" (0.001708)
121. feature "HSPH1" (0.001695)
122. feature "TPM3" (0.001642)
123. feature "NDUFB2" (0.001600)
124. feature "H1-3" (0.001539)
125. feature "RPL28" (0.001531)
126. feature "PFDN4" (0.001521)
127. feature "RPS8" (0.001489)
128. feature "TMSB4X" (0.001465)
129. feature "RPS3" (0.001457)
130. feature "ATP5MK" (0.001435)
131. feature "CACNA1A" (0.001418)
132. feature "PTP4A2" (0.001414)
133. feature "EZR" (0.001410)
134. feature "EGLN3" (0.001409)
135. feature "GOLGA4" (0.001397)
136. feature "RPL11" (0.001386)
137. feature "ATP5ME" (0.001381)
138. feature "HDGF" (0.001378)
139. feature "H1-5" (0.001372)
140. feature "PTPN11" (0.001370)
141. feature "RPS6KB1" (0.001369)
142. feature "MT1X" (0.001365)
143. feature "RPL21" (0.001338)
144. feature "PRRG3" (0.001307)
145. feature "CALR" (0.001300)
146. feature "DEK" (0.001291)
147. feature "XBP1" (0.001260)
148. feature "LARP1" (0.001227)
149. feature "NCALD" (0.001196)
150. feature "ANP32B" (0.001191)
151. feature "GSTP1" (0.001182)
152. feature "BPTF" (0.001181)
153. feature "MKI67" (0.001178)
154. feature "ATP1A1" (0.001169)
155. feature "H1-4" (0.001169)
156. feature "HIF1A" (0.001158)
157. feature "RPL18" (0.001153)
158. feature "NEAT1" (0.001145)
159. feature "MT-ND6" (0.001140)
160. feature "ZNF302" (0.001130)
161. feature "RPS21" (0.001128)
162. feature "C4orf48" (0.001126)
163. feature "TPM4" (0.001108)
164. feature "H2AC12" (0.001107)
165. feature "PDIA3" (0.001103)
166. feature "MT-TQ" (0.001099)
167. feature "CENPF" (0.001092)
168. feature "MT-ND2" (0.001084)
169. feature "BTBD9" (0.001073)
170. feature "POLR2L" (0.001069)
171. feature "PABPC1" (0.001053)
172. feature "CAPZA1" (0.001051)
173. feature "DSCAM-AS1" (0.001049)
174. feature "HNRNPM" (0.001046)
175. feature "FOXP1" (0.001042)
176. feature "RAC1" (0.001027)
177. feature "HSPA5" (0.001026)
178. feature "EEF2" (0.001023)
179. feature "COX7C" (0.001013)
180. feature "S100A2" (0.001007)
181. feature "RPSA" (0.001003)
182. feature "CKS2" (0.000995)
183. feature "H1-2" (0.000991)
184. feature "ATAD2" (0.000975)
185. feature "DNMT1" (0.000962)
186. feature "PDAP1" (0.000954)
187. feature "NSD1" (0.000948)
188. feature "ATP5F1E" (0.000939)
189. feature "RPL18A" (0.000929)
190. feature "ATP5MG" (0.000920)
191. feature "LARP4B" (0.000918)
192. feature "GNL3L" (0.000910)
193. feature "MT-CO1" (0.000909)
194. feature "RPL29" (0.000877)
195. feature "TOP2A" (0.000873)
196. feature "STMN1" (0.000866)
197. feature "RPS29" (0.000866)
198. feature "NFE2L2" (0.000865)
199. feature "TPX2" (0.000856)
200. feature "EIF5B" (0.000851)
201. feature "GATA3" (0.000824)
202. feature "KIF5B" (0.000821)
203. feature "PPDPF" (0.000810)
204. feature "PLEC" (0.000810)
205. feature "KRT7" (0.000806)
206. feature "COPS9" (0.000800)
207. feature "APLP2" (0.000800)
208. feature "HMGB2" (0.000791)
209. feature "H1-1" (0.000788)
210. feature "H2AC20" (0.000787)
211. feature "EEF1A1" (0.000767)
212. feature "TRIM44" (0.000764)
213. feature "RTF1" (0.000762)
214. feature "C9orf78" (0.000761)
215. feature "NMD3" (0.000752)
216. feature "PAK2" (0.000729)
217. feature "DAAM1" (0.000725)
218. feature "MYH9" (0.000707)
219. feature "PPM1G" (0.000695)
220. feature "NEDD8" (0.000685)
221. feature "POLR2J" (0.000684)
222. feature "RHOBTB3" (0.000678)
223. feature "GTF3A" (0.000677)
224. feature "PPP1R14B" (0.000672)
225. feature "BAP1" (0.000669)
226. feature "ZBTB20" (0.000664)
227. feature "C7orf50" (0.000662)
228. feature "NRBP1" (0.000656)
229. feature "KRT10" (0.000655)
230. feature "ERO1A" (0.000652)
231. feature "ARF3" (0.000650)
232. feature "VMP1" (0.000650)
233. feature "ARPP19" (0.000647)
234. feature "AREG" (0.000642)
235. feature "BAZ1B" (0.000642)
236. feature "MT-TV" (0.000634)
237. feature "UQCR11" (0.000632)
238. feature "UQCC2" (0.000622)
239. feature "MT-ND1" (0.000621)
240. feature "PPIG" (0.000620)
241. feature "PFN1" (0.000618)
242. feature "KCNJ3" (0.000618)
243. feature "PLOD2" (0.000617)
244. feature "UBC" (0.000612)
245. feature "NEDD4L" (0.000610)
246. feature "TPM1" (0.000608)
247. feature "AHNAK" (0.000608)
248. feature "FABP5" (0.000607)
249. feature "ACTG1" (0.000604)
250. feature "ZNF217" (0.000600)
251. feature "H2AC11" (0.000596)
252. feature "BRD4" (0.000596)
253. feature "C19orf53" (0.000595)
254. feature "HMGA1" (0.000595)
255. feature "YBX3" (0.000590)
256. feature "MT-ATP8" (0.000589)
257. feature "STRBP" (0.000587)
258. feature "CNNM2" (0.000586)
259. feature "HEPACAM" (0.000584)
260. feature "SLC9A3R1" (0.000582)
261. feature "CSTB" (0.000581)
262. feature "NRIP1" (0.000581)
263. feature "LIMCH1" (0.000581)
264. feature "DDIT4" (0.000571)
265. feature "SMC4" (0.000565)
266. feature "PDIA6" (0.000564)
267. feature "HSBP1" (0.000560)
268. feature "C4orf3" (0.000559)
269. feature "MGRN1" (0.000554)
270. feature "EIF3A" (0.000549)
271. feature "NAA50" (0.000548)
272. feature "NCOR2" (0.000543)
273. feature "KCNJ2" (0.000541)
274. feature "H3-3A" (0.000538)
275. feature "SLC25A24" (0.000537)
276. feature "GNAS" (0.000537)
277. feature "FOSL1" (0.000530)
278. feature "RAD23B" (0.000528)
279. feature "PAPOLA" (0.000524)
280. feature "CDV3" (0.000518)
281. feature "CLIP1" (0.000514)
282. feature "G6PD" (0.000512)
283. feature "TRIP12" (0.000511)
284. feature "OAZ1" (0.000508)
285. feature "MRPL33" (0.000506)
286. feature "IGFBP5" (0.000506)
287. feature "LEO1" (0.000505)
288. feature "KDM1A" (0.000501)
289. feature "ANXA1" (0.000497)
290. feature "AURKA" (0.000497)
291. feature "CEP55" (0.000497)
292. feature "GDPGP1" (0.000493)
293. feature "SPATS2L" (0.000488)
294. feature "KRT14" (0.000485)
295. feature "HSPA4" (0.000484)
296. feature "LAMB3" (0.000483)
297. feature "SVIP" (0.000483)
298. feature "KRT13" (0.000480)
299. feature "TAOK3" (0.000476)
300. feature "METAP2" (0.000476)
301. feature "MZT2B" (0.000473)
302. feature "POLR2A" (0.000472)
303. feature "SFN" (0.000471)
304. feature "S100P" (0.000471)
305. feature "MAP3K13" (0.000468)
306. feature "RNPS1" (0.000467)
307. feature "NBN" (0.000466)
308. feature "PRMT2" (0.000461)
309. feature "LRRC59" (0.000460)
310. feature "H19" (0.000459)
311. feature "CAVIN1" (0.000458)
312. feature "HCFC1" (0.000453)
313. feature "PTBP3" (0.000452)
314. feature "SNX27" (0.000443)
315. feature "GSPT1" (0.000442)
316. feature "EIF2AK1" (0.000439)
317. feature "PLBD2" (0.000438)
318. feature "ENAH" (0.000435)
319. feature "SLC2A1" (0.000431)
320. feature "CDK2AP1" (0.000426)
321. feature "FAM162A" (0.000424)
322. feature "TSR1" (0.000424)
323. feature "CDKN3" (0.000421)
324. feature "H1-0" (0.000418)
325. feature "BASP1" (0.000412)
326. feature "C8orf33" (0.000412)
327. feature "GPATCH4" (0.000410)
328. feature "GBP1P1" (0.000408)
329. feature "MARCHF6" (0.000406)
330. feature "CSTA" (0.000404)
331. feature "ATMIN" (0.000403)
332. feature "LINC01304" (0.000400)
333. feature "CAMSAP2" (0.000396)
334. feature "CD47" (0.000395)
335. feature "TBCA" (0.000394)
336. feature "NDUFB10" (0.000394)
337. feature "MED13" (0.000388)
338. feature "KRT5" (0.000386)
339. feature "CAVIN3" (0.000385)
340. feature "RALBP1" (0.000384)
341. feature "APOOL" (0.000382)
342. feature "RPS27L" (0.000382)
343. feature "PRPF40A" (0.000380)
344. feature "MAN1A2" (0.000379)
345. feature "TCEAL4" (0.000378)
346. feature "RPL27" (0.000377)
347. feature "TMA7" (0.000377)
348. feature "AP2S1" (0.000377)
349. feature "METRN" (0.000375)
350. feature "TAF13" (0.000374)
351. feature "RAB13" (0.000373)
352. feature "RAD21" (0.000372)
353. feature "SLK" (0.000370)
354. feature "HNRNPUL1" (0.000370)
355. feature "TAF15" (0.000368)
356. feature "WWP1" (0.000364)
357. feature "AP1G1" (0.000364)
358. feature "LBH" (0.000363)
359. feature "CCNI" (0.000362)
360. feature "PMEPA1" (0.000361)
361. feature "RPS6KA6" (0.000360)
362. feature "PSMD14" (0.000359)
363. feature "FAM120A" (0.000358)
364. feature "TARS1" (0.000355)
365. feature "ZHX1" (0.000354)
366. feature "MYL12B" (0.000353)
367. feature "URI1" (0.000353)
368. feature "DKC1" (0.000353)
369. feature "ROCK2" (0.000352)
370. feature "LXN" (0.000352)
371. feature "CENPU" (0.000349)
372. feature "ZFP36L1" (0.000348)
373. feature "SYNE2" (0.000348)
374. feature "GUK1" (0.000347)
375. feature "MYO5C" (0.000347)
376. feature "ANP32E" (0.000345)
377. feature "BTN3A2" (0.000344)
378. feature "PROSER1" (0.000343)
379. feature "H4C5" (0.000342)
380. feature "RBM25" (0.000342)
381. feature "NDUFS6" (0.000340)
382. feature "PRRC2A" (0.000339)
383. feature "DDX21" (0.000337)
384. feature "TIMM8B" (0.000337)
385. feature "MLLT3" (0.000336)
386. feature "POLR2I" (0.000331)
387. feature "FAM83A" (0.000331)
388. feature "S100A16" (0.000330)
389. feature "JAK1" (0.000330)
390. feature "HNRNPD" (0.000329)
391. feature "PAIP2" (0.000328)
392. feature "MRPS34" (0.000327)
393. feature "ATOX1" (0.000327)
394. feature "DDX54" (0.000326)
395. feature "PHF3" (0.000326)
396. feature "SYTL2" (0.000325)
397. feature "SETD2" (0.000323)
398. feature "BOD1L1" (0.000321)
399. feature "MAP7" (0.000320)
400. feature "PUS7" (0.000317)
401. feature "METTL26" (0.000317)
402. feature "USP8" (0.000315)
403. feature "ANKRD11" (0.000313)
404. feature "REST" (0.000313)
405. feature "JUP" (0.000311)
406. feature "IRAK1" (0.000311)
407. feature "HILPDA" (0.000311)
408. feature "RB1CC1" (0.000310)
409. feature "DYNLL2" (0.000309)
410. feature "ZNRF1" (0.000309)
411. feature "MTND1P23" (0.000308)
412. feature "CCDC34" (0.000308)
413. feature "NUP62" (0.000307)
414. feature "ZNF121" (0.000307)
415. feature "CDK2AP2" (0.000304)
416. feature "CTBP2" (0.000303)
417. feature "RPS26" (0.000303)
418. feature "ANKRD52" (0.000303)
419. feature "FAM177A1" (0.000300)
420. feature "CAMK2N1" (0.000300)
421. feature "PDLIM1" (0.000299)
422. feature "RAP2B" (0.000299)
423. feature "VTI1B" (0.000298)
424. feature "GADD45GIP1" (0.000297)
425. feature "MT1E" (0.000297)
426. feature "C6orf62" (0.000296)
427. feature "RABEP1" (0.000296)
428. feature "FOSL2" (0.000294)
429. feature "CAP1" (0.000294)
430. feature "H2BC4" (0.000294)
431. feature "EPPK1" (0.000293)
432. feature "UBE2Q1" (0.000291)
433. feature "CLTB" (0.000291)
434. feature "KPNA1" (0.000291)
435. feature "TCEAL9" (0.000290)
436. feature "UBE2K" (0.000290)
437. feature "HTATSF1" (0.000289)
438. feature "KPNA2" (0.000289)
439. feature "SLTM" (0.000289)
440. feature "H2AZ2" (0.000287)
441. feature "EEA1" (0.000287)
442. feature "SENP7" (0.000287)
443. feature "CENPB" (0.000285)
444. feature "ZMYND11" (0.000285)
445. feature "UGDH" (0.000284)
446. feature "PHACTR2" (0.000284)
447. feature "SOS1" (0.000281)
448. feature "SEPTIN9" (0.000281)
449. feature "RBM23" (0.000281)
450. feature "RPL7A" (0.000281)
451. feature "GOLGA3" (0.000280)
452. feature "KMT2D" (0.000279)
453. feature "ITPRID2" (0.000279)
454. feature "USP32" (0.000278)
455. feature "NUDT1" (0.000278)
456. feature "RGPD4-AS1" (0.000277)
457. feature "ATRX" (0.000275)
458. feature "FLNA" (0.000274)
459. feature "CSK" (0.000274)
460. feature "SYT14" (0.000274)
461. feature "ANP32A" (0.000273)
462. feature "TAGLN2" (0.000273)
463. feature "ROCK1" (0.000271)
464. feature "GPX4" (0.000271)
465. feature "NIPBL" (0.000271)
466. feature "MDM2" (0.000270)
467. feature "LUZP1" (0.000269)
468. feature "DCTN1" (0.000269)
469. feature "LRRFIP2" (0.000269)
470. feature "RHOD" (0.000269)
471. feature "DTYMK" (0.000267)
472. feature "CMSS1" (0.000266)
473. feature "LMNB2" (0.000265)
474. feature "PAQR8" (0.000265)
475. feature "SUDS3" (0.000265)
476. feature "HNRNPH3" (0.000265)
477. feature "MZT2A" (0.000264)
478. feature "STC2" (0.000264)
479. feature "SLC6A8" (0.000264)
480. feature "OPTN" (0.000263)
481. feature "SLC25A48" (0.000262)
482. feature "VIT" (0.000262)
483. feature "FAM102A" (0.000262)
484. feature "ILF3" (0.000262)
485. feature "CARMIL1" (0.000262)
486. feature "ZBTB7A" (0.000261)
487. feature "H3C2" (0.000261)
488. feature "NORAD" (0.000261)
489. feature "PGAM1" (0.000259)
490. feature "ZC3H13" (0.000259)
491. feature "ZCRB1" (0.000258)
492. feature "GATAD2A" (0.000258)
493. feature "NONO" (0.000256)
494. feature "RBBP6" (0.000256)
495. feature "PFKP" (0.000256)
496. feature "DNAJC2" (0.000256)
497. feature "OIP5-AS1" (0.000255)
498. feature "NCOR1" (0.000254)
499. feature "FGFR1OP2" (0.000254)
500. feature "SSRP1" (0.000254)
501. feature "TNIP2" (0.000254)
502. feature "SUMO3" (0.000253)
503. feature "CNBP" (0.000253)
504. feature "MT-TS1" (0.000253)
505. feature "ZNF688" (0.000253)
506. feature "R3HDM2" (0.000253)
507. feature "SART1" (0.000252)
508. feature "SIVA1" (0.000252)
509. feature "SRGAP1" (0.000252)
510. feature "FEM1A" (0.000250)
511. feature "SRM" (0.000250)
512. feature "SEM1" (0.000249)
513. feature "FUBP3" (0.000248)
514. feature "PSMC5" (0.000247)
515. feature "SMIM10L1" (0.000247)
516. feature "SLC25A37" (0.000245)
517. feature "ZC3H18" (0.000245)
518. feature "ATN1" (0.000244)
519. feature "MRPL55" (0.000242)
520. feature "YTHDC1" (0.000241)
521. feature "DBF4" (0.000241)
522. feature "AP1M1" (0.000240)
523. feature "KIF1C" (0.000239)
524. feature "KPNA4" (0.000237)
525. feature "PHLDA2" (0.000235)
526. feature "AKT1S1" (0.000232)
527. feature "RSRC2" (0.000231)
528. feature "NACC1" (0.000231)
529. feature "MTCH1" (0.000231)
530. feature "CMIP" (0.000231)
531. feature "CDC37" (0.000231)
532. feature "PKP4" (0.000231)
533. feature "TSPYL1" (0.000231)
534. feature "PAQR7" (0.000230)
535. feature "MCM4" (0.000230)
536. feature "ZNF480" (0.000229)
537. feature "SNHG9" (0.000229)
538. feature "ANKIB1" (0.000229)
539. feature "WNK1" (0.000229)
540. feature "MAP4K4" (0.000229)
541. feature "PRKACA" (0.000228)
542. feature "COTL1" (0.000228)
543. feature "CTNND1" (0.000228)
544. feature "ETF1" (0.000227)
545. feature "KAT7" (0.000227)
546. feature "RBX1" (0.000226)
547. feature "PSAP" (0.000224)
548. feature "COX8A" (0.000223)
549. feature "TNRC6A" (0.000223)
550. feature "MAFF" (0.000223)
551. feature "FUT11" (0.000223)
552. feature "SHOX" (0.000223)
553. feature "BRIP1" (0.000222)
554. feature "MYBL2" (0.000222)
555. feature "STARD7" (0.000222)
556. feature "SNRNP70" (0.000221)
557. feature "MXI1" (0.000221)
558. feature "KMT5B" (0.000220)
559. feature "SPIN1" (0.000220)
560. feature "JUN" (0.000219)
561. feature "IFI27L2" (0.000219)
562. feature "THRB" (0.000219)
563. feature "KIF3B" (0.000219)
564. feature "RANGAP1" (0.000218)
565. feature "FAM107B" (0.000218)
566. feature "NDUFAF8" (0.000214)
567. feature "KIF21A" (0.000214)
568. feature "CNN3" (0.000212)
569. feature "NFIC" (0.000212)
570. feature "FGD5-AS1" (0.000212)
571. feature "UPF3B" (0.000211)
572. feature "RRS1" (0.000211)
573. feature "KRT17" (0.000211)
574. feature "C5orf63" (0.000211)
575. feature "TMEM160" (0.000211)
576. feature "PDCD4" (0.000211)
577. feature "NIBAN2" (0.000210)
578. feature "HMGN3" (0.000210)
579. feature "EGR1" (0.000210)
580. feature "BDP1" (0.000210)
581. feature "SUPT5H" (0.000210)
582. feature "DNAJC21" (0.000209)
583. feature "PPP4R3A" (0.000207)
584. feature "SPAG9" (0.000206)
585. feature "ARID5B" (0.000206)
586. feature "SMARCC1" (0.000206)
587. feature "CDC42BPA" (0.000206)
588. feature "SMARCB1" (0.000206)
589. feature "ARID4B" (0.000205)
590. feature "AMOTL1" (0.000204)
591. feature "TSPO" (0.000204)
592. feature "INPP4B" (0.000204)
593. feature "KIAA1522" (0.000203)
594. feature "UBE2Q2" (0.000203)
595. feature "PALLD" (0.000202)
596. feature "GSE1" (0.000202)
597. feature "ZNF316" (0.000202)
598. feature "SMC6" (0.000201)
599. feature "ISCU" (0.000201)
600. feature "ZMIZ1" (0.000201)
601. feature "USP1" (0.000200)
602. feature "FASN" (0.000200)
603. feature "WBP4" (0.000199)
604. feature "GNB1" (0.000199)
605. feature "CCDC167" (0.000199)
606. feature "PPP4R2" (0.000199)
607. feature "STARD10" (0.000198)
608. feature "GOLGA5" (0.000197)
609. feature "MTA2" (0.000196)
610. feature "SAMD4A" (0.000196)
611. feature "BCCIP" (0.000196)
612. feature "SCRIB" (0.000196)
613. feature "WSB2" (0.000195)
614. feature "MPHOSPH6" (0.000195)
615. feature "LYAR" (0.000195)
616. feature "TNIP1" (0.000195)
617. feature "PREX1" (0.000195)
618. feature "PFKFB3" (0.000194)
619. feature "YTHDF1" (0.000194)
620. feature "AP3D1" (0.000194)
621. feature "NMT1" (0.000192)
622. feature "PPP2R5E" (0.000192)
623. feature "ZNF703" (0.000192)
624. feature "RRAS" (0.000192)
625. feature "MIR4458HG" (0.000192)
626. feature "UQCC3" (0.000192)
627. feature "NINJ1" (0.000192)
628. feature "TMEM238" (0.000191)
629. feature "IGFL2-AS1" (0.000190)
630. feature "EIF4E" (0.000190)
631. feature "R3HDM1" (0.000190)
632. feature "TUBD1" (0.000190)
633. feature "NIN" (0.000189)
634. feature "EPB41L1" (0.000189)
635. feature "TASOR2" (0.000189)
636. feature "RBFOX2" (0.000189)
637. feature "PCP4" (0.000188)
638. feature "HK1" (0.000188)
639. feature "FBP1" (0.000188)
640. feature "RFC1" (0.000188)
641. feature "RBBP8" (0.000187)
642. feature "ANKRD17" (0.000187)
643. feature "CFAP36" (0.000186)
644. feature "TOP2B" (0.000186)
645. feature "CRNDE" (0.000185)
646. feature "CHMP4B" (0.000185)
647. feature "DDX23" (0.000185)
648. feature "CFAP251" (0.000184)
649. feature "KRT80" (0.000183)
650. feature "CDC42SE1" (0.000183)
651. feature "PYGO2" (0.000183)
652. feature "NR2F6" (0.000183)
653. feature "HCFC1R1" (0.000182)
654. feature "SENP6" (0.000182)
655. feature "FAM32A" (0.000181)
656. feature "ATP2A2" (0.000180)
657. feature "TLK2" (0.000180)
658. feature "MT-TY" (0.000180)
659. feature "NET1" (0.000179)
660. feature "ARHGDIA" (0.000178)
661. feature "MIR663AHG" (0.000178)
662. feature "NECAB1" (0.000178)
663. feature "HSPBP1" (0.000177)
664. feature "SMARCD2" (0.000177)
665. feature "SMG7" (0.000177)
666. feature "PHF20L1" (0.000177)
667. feature "PRR11" (0.000177)
668. feature "CLIC1" (0.000176)
669. feature "MDM4" (0.000176)
670. feature "CASP8AP2" (0.000175)
671. feature "NMT2" (0.000175)
672. feature "GNAQ" (0.000175)
673. feature "ID1" (0.000175)
674. feature "MAVS" (0.000175)
675. feature "CLSPN" (0.000174)
676. feature "EIF2AK2" (0.000174)
677. feature "TF" (0.000174)
678. feature "TIMELESS" (0.000173)
679. feature "NDUFB1" (0.000173)
680. feature "RAB35" (0.000172)
681. feature "LAGE3" (0.000172)
682. feature "ANAPC16" (0.000172)
683. feature "PRPF19" (0.000172)
684. feature "ESCO1" (0.000172)
685. feature "IRF2BPL" (0.000171)
686. feature "RRP15" (0.000171)
687. feature "ARHGAP5" (0.000170)
688. feature "MRPL14" (0.000170)
689. feature "HK2" (0.000170)
690. feature "CENPE" (0.000170)
691. feature "HNRNPA1" (0.000169)
692. feature "SAMD4B" (0.000168)
693. feature "CETN3" (0.000167)
694. feature "NEUROD1" (0.000167)
695. feature "ITGA3" (0.000167)
696. feature "KCNQ1OT1" (0.000167)
697. feature "DNMT3A" (0.000166)
698. feature "ZNF148" (0.000166)
699. feature "MYBL1" (0.000165)
700. feature "DDIT3" (0.000165)
701. feature "GARS1" (0.000165)
702. feature "GYS1" (0.000165)
703. feature "SOCS4" (0.000165)
704. feature "CPSF2" (0.000164)
705. feature "PPFIA1" (0.000164)
706. feature "CDC42BPB" (0.000164)
707. feature "PABPC4" (0.000164)
708. feature "YES1" (0.000164)
709. feature "MSANTD3" (0.000164)
710. feature "CBX6" (0.000164)
711. feature "SPDL1" (0.000163)
712. feature "ARL13B" (0.000163)
713. feature "POLE4" (0.000163)
714. feature "PDXK" (0.000163)
715. feature "USP7" (0.000162)
716. feature "LINC02367" (0.000162)
717. feature "SECISBP2" (0.000162)
718. feature "RAD54L2" (0.000162)
719. feature "S100A13" (0.000162)
720. feature "NSRP1" (0.000161)
721. feature "ITPK1" (0.000161)
722. feature "EBAG9" (0.000161)
723. feature "PACS1" (0.000161)
724. feature "EIF4EBP2" (0.000161)
725. feature "PRR34-AS1" (0.000161)
726. feature "GCC2" (0.000161)
727. feature "SKI" (0.000161)
728. feature "CCDC186" (0.000160)
729. feature "MRPS2" (0.000160)
730. feature "CHD8" (0.000159)
731. feature "SOGA1" (0.000159)
732. feature "HMGN5" (0.000159)
733. feature "UTP18" (0.000159)
734. feature "VRK3" (0.000158)
735. feature "TAOK1" (0.000158)
736. feature "H2AC16" (0.000158)
737. feature "UBP1" (0.000158)
738. feature "BAG3" (0.000156)
739. feature "RP9" (0.000156)
740. feature "H2BC9" (0.000156)
741. feature "RASGEF1B" (0.000156)
742. feature "AK4" (0.000155)
743. feature "NELFB" (0.000155)
744. feature "CAPZB" (0.000155)
745. feature "PRDM2" (0.000155)
746. feature "RAB11B" (0.000155)
747. feature "RNF168" (0.000155)
748. feature "SLPI" (0.000154)
749. feature "MARK2" (0.000153)
750. feature "RHOA" (0.000153)
751. feature "CMAS" (0.000153)
752. feature "UGCG" (0.000152)
753. feature "TIMP3" (0.000152)
754. feature "GDI1" (0.000152)
755. feature "MGP" (0.000151)
756. feature "MORN2" (0.000151)
757. feature "CAPN1" (0.000151)
758. feature "SMIM15" (0.000150)
759. feature "TWNK" (0.000149)
760. feature "SYNJ2" (0.000149)
761. feature "FBXW5" (0.000149)
762. feature "BRD7" (0.000149)
763. feature "RCC2" (0.000148)
764. feature "ZNF462" (0.000148)
765. feature "IGFBP4" (0.000148)
766. feature "EFNA2" (0.000148)
767. feature "EIF4G3" (0.000147)
768. feature "NAA10" (0.000147)
769. feature "CEP63" (0.000147)
770. feature "MT-TE" (0.000147)
771. feature "CITED2" (0.000147)
772. feature "BLCAP" (0.000146)
773. feature "PHLDB2" (0.000146)
774. feature "KDM3A" (0.000145)
775. feature "COX14" (0.000145)
776. feature "ZFC3H1" (0.000145)
777. feature "LOXL2" (0.000145)
778. feature "SNRPA" (0.000145)
779. feature "SERINC2" (0.000145)
780. feature "SIGMAR1" (0.000144)
781. feature "STX2" (0.000144)
782. feature "PLEKHB2" (0.000144)
783. feature "AHCYL1" (0.000144)
784. feature "FAM20B" (0.000143)
785. feature "MRPS33" (0.000143)
786. feature "AXL" (0.000143)
787. feature "NAA15" (0.000143)
788. feature "PHGDH" (0.000142)
789. feature "TCF20" (0.000142)
790. feature "TK1" (0.000142)
791. feature "COMMD6" (0.000142)
792. feature "SUPT6H" (0.000142)
793. feature "UTP3" (0.000142)
794. feature "MBD2" (0.000141)
795. feature "ISOC2" (0.000141)
796. feature "CTXN1" (0.000141)
797. feature "MRGBP" (0.000141)
798. feature "ITSN1" (0.000141)
799. feature "FASTK" (0.000140)
800. feature "WIPF2" (0.000140)
801. feature "SLC4A1" (0.000139)
802. feature "RNF20" (0.000139)
803. feature "NARF" (0.000139)
804. feature "RAB4A" (0.000139)
805. feature "MYO10" (0.000139)
806. feature "LINC01133" (0.000138)
807. feature "CCDC50" (0.000138)
808. feature "BRI3" (0.000138)
809. feature "ULK1" (0.000137)
810. feature "EFNA5" (0.000137)
811. feature "MT-TP" (0.000137)
812. feature "KIF14" (0.000136)
813. feature "ZCCHC9" (0.000136)
814. feature "MYH14" (0.000136)
815. feature "GTF2F1" (0.000135)
816. feature "NEUROD2" (0.000135)
817. feature "LSM4" (0.000135)
818. feature "BAZ2A" (0.000135)
819. feature "TMEM141" (0.000134)
820. feature "PRMT6" (0.000134)
821. feature "LARP7" (0.000134)
822. feature "SLC25A23" (0.000134)
823. feature "C12orf57" (0.000133)
824. feature "NDUFA2" (0.000133)
825. feature "ARHGEF26" (0.000133)
826. feature "CNOT9" (0.000133)
827. feature "LETM1" (0.000132)
828. feature "CNTRL" (0.000132)
829. feature "ESRP2" (0.000132)
830. feature "DYNC2I2" (0.000132)
831. feature "CSRP1" (0.000131)
832. feature "DNAJA2" (0.000131)
833. feature "ZNF652" (0.000131)
834. feature "RELA" (0.000131)
835. feature "MAGI3" (0.000131)
836. feature "RNF122" (0.000130)
837. feature "ZNF609" (0.000130)
838. feature "ZMYND8" (0.000130)
839. feature "MAPKAPK2" (0.000130)
840. feature "ARHGEF12" (0.000130)
841. feature "CNOT6L" (0.000130)
842. feature "TTL" (0.000129)
843. feature "SGO2" (0.000129)
844. feature "PDS5A" (0.000129)
845. feature "SPRY1" (0.000129)
846. feature "VAV2" (0.000129)
847. feature "ATP6V1E1" (0.000128)
848. feature "RAB12" (0.000128)
849. feature "SLITRK5" (0.000128)
850. feature "CEP83" (0.000128)
851. feature "CDKN1A" (0.000128)
852. feature "HRAS" (0.000128)
853. feature "BROX" (0.000128)
854. feature "FAM50A" (0.000127)
855. feature "ITGA5" (0.000127)
856. feature "RPL12P38" (0.000127)
857. feature "FAM111B" (0.000126)
858. feature "LINC01278" (0.000126)
859. feature "DPCD" (0.000126)
860. feature "SPRYD3" (0.000126)
861. feature "ODF2" (0.000126)
862. feature "EHD2" (0.000126)
863. feature "EPB41L2" (0.000126)
864. feature "GPBP1" (0.000126)
865. feature "REV3L" (0.000125)
866. feature "FAM200B" (0.000125)
867. feature "SEPTIN11" (0.000125)
868. feature "PPM1K" (0.000124)
869. feature "NCBP3" (0.000124)
870. feature "TNFSF13B" (0.000124)
871. feature "DRAP1" (0.000124)
872. feature "RASSF3" (0.000124)
873. feature "CIRBP" (0.000124)
874. feature "BOLA3" (0.000124)
875. feature "FKBP5" (0.000123)
876. feature "SRI" (0.000123)
877. feature "RRP1B" (0.000123)
878. feature "DHX37" (0.000122)
879. feature "CALB1" (0.000122)
880. feature "TINAGL1" (0.000122)
881. feature "H2AX" (0.000122)
882. feature "KEAP1" (0.000122)
883. feature "ELOA" (0.000122)
884. feature "INHBA" (0.000122)
885. feature "LINC02762" (0.000122)
886. feature "PSME4" (0.000121)
887. feature "BCAR1" (0.000121)
888. feature "ZC3H7A" (0.000121)
889. feature "ATXN2L" (0.000121)
890. feature "EIF2B4" (0.000121)
891. feature "RNF10" (0.000121)
892. feature "RAB5B" (0.000121)
893. feature "MAP3K2" (0.000120)
894. feature "STUB1" (0.000120)
895. feature "ZNF318" (0.000120)
896. feature "MNS1" (0.000120)
897. feature "PTGR1" (0.000120)
898. feature "IWS1" (0.000120)
899. feature "SLMAP" (0.000120)
900. feature "VAT1" (0.000120)
901. feature "ARFGEF1" (0.000120)
902. feature "CEP78" (0.000119)
903. feature "GABPB2" (0.000119)
904. feature "SINHCAFP3" (0.000119)
905. feature "FGF12" (0.000119)
906. feature "UIMC1" (0.000119)
907. feature "GSS" (0.000119)
908. feature "INCENP" (0.000119)
909. feature "EMP2" (0.000119)
910. feature "ZNF146" (0.000119)
911. feature "PLCXD2" (0.000118)
912. feature "HIF3A" (0.000118)
913. feature "BMS1" (0.000118)
914. feature "PABPN1" (0.000118)
915. feature "AFF4" (0.000118)
916. feature "HOXB7" (0.000118)
917. feature "LPP" (0.000118)
918. feature "PCBD1" (0.000118)
919. feature "C2orf49" (0.000117)
920. feature "FAM83H" (0.000117)
921. feature "FARSA" (0.000117)
922. feature "ZNF326" (0.000117)
923. feature "MDC1" (0.000117)
924. feature "FOXP4" (0.000117)
925. feature "CERS2" (0.000117)
926. feature "UBE2C" (0.000116)
927. feature "MARK3" (0.000116)
928. feature "GMCL1" (0.000116)
929. feature "C12orf75" (0.000116)
930. feature "VEGFB" (0.000116)
931. feature "OXLD1" (0.000116)
932. feature "CCDC88A" (0.000115)
933. feature "DCTN2" (0.000115)
934. feature "MGMT" (0.000115)
935. feature "ARHGAP1" (0.000115)
936. feature "ADAM15" (0.000114)
937. feature "ALKBH5" (0.000114)
938. feature "PCYT1A" (0.000114)
939. feature "CD151" (0.000114)
940. feature "INO80E" (0.000114)
941. feature "TASOR" (0.000113)
942. feature "SHISA5" (0.000113)
943. feature "MOB3A" (0.000113)
944. feature "GIPC1" (0.000113)
945. feature "POLDIP2" (0.000113)
946. feature "PTP4A1" (0.000113)
947. feature "PA2G4P6" (0.000113)
948. feature "EPAS1" (0.000113)
949. feature "SETD3" (0.000112)
950. feature "TAF10" (0.000112)
951. feature "PSMD13" (0.000112)
952. feature "LINC01291" (0.000112)
953. feature "MAP3K4" (0.000111)
954. feature "NCOA1" (0.000111)
955. feature "MAGEF1" (0.000111)
956. feature "ILRUN" (0.000111)
957. feature "MCRIP2" (0.000111)
958. feature "SAC3D1" (0.000111)
959. feature "RPLP0P2" (0.000110)
960. feature "ING2" (0.000110)
961. feature "SCNM1" (0.000110)
962. feature "FAM199X" (0.000110)
963. feature "DNM2" (0.000110)
964. feature "NSD2" (0.000110)
965. feature "ANKRD40" (0.000109)
966. feature "MCL1" (0.000109)
967. feature "TSR3" (0.000109)
968. feature "ERF" (0.000109)
969. feature "LCLAT1" (0.000109)
970. feature "C20orf27" (0.000109)
971. feature "CBX4" (0.000109)
972. feature "ZYX" (0.000109)
973. feature "YKT6" (0.000108)
974. feature "FAM214A" (0.000108)
975. feature "MSR1" (0.000108)
976. feature "PPP1R3G" (0.000108)
977. feature "BMPR1B" (0.000108)
978. feature "RPS19BP1" (0.000108)
979. feature "DCAF6" (0.000108)
980. feature "THOC7" (0.000108)
981. feature "MT-TA" (0.000108)
982. feature "SMDT1" (0.000107)
983. feature "CCDC92" (0.000107)
984. feature "DCAF7" (0.000107)
985. feature "BICD1" (0.000107)
986. feature "CDK5RAP2" (0.000106)
987. feature "GALNT1" (0.000106)
988. feature "DGKZ" (0.000106)
989. feature "CYB5R3" (0.000106)
990. feature "ZBTB45" (0.000106)
991. feature "RAD51" (0.000106)
992. feature "PAPSS2" (0.000106)
993. feature "SH3RF1" (0.000106)
994. feature "ARIH1" (0.000106)
995. feature "CDK12" (0.000106)
996. feature "MBD6" (0.000105)
997. feature "MAST4" (0.000105)
998. feature "SENP5" (0.000105)
999. feature "TUBA1C" (0.000105)
1000. feature "RIPK2" (0.000105)
1001. feature "FMR1" (0.000105)
1002. feature "PI4KB" (0.000105)
1003. feature "SEC61G" (0.000105)
1004. feature "DGUOK" (0.000105)
1005. feature "MAPK14" (0.000104)
1006. feature "HIRIP3" (0.000104)
1007. feature "DHTKD1" (0.000104)
1008. feature "RFK" (0.000104)
1009. feature "AAMP" (0.000103)
1010. feature "NCKAP1" (0.000103)
1011. feature "KCTD20" (0.000103)
1012. feature "PTGES2" (0.000103)
1013. feature "MINK1" (0.000103)
1014. feature "CDA" (0.000102)
1015. feature "RPSAP48" (0.000102)
1016. feature "SCP2" (0.000102)
1017. feature "GALM" (0.000102)
1018. feature "SLC25A1" (0.000102)
1019. feature "NEK2" (0.000102)
1020. feature "KHSRP" (0.000101)
1021. feature "ZBTB38" (0.000101)
1022. feature "GRN" (0.000101)
1023. feature "YTHDF3" (0.000101)
1024. feature "TAF3" (0.000101)
1025. feature "PITX1" (0.000101)
1026. feature "IQCE" (0.000101)
1027. feature "SUSD6" (0.000101)
1028. feature "PSIP1" (0.000101)
1029. feature "CFAP97" (0.000101)
1030. feature "GDF15" (0.000100)
1031. feature "LARP1B" (0.000100)
1032. feature "STK11" (0.000100)
1033. feature "CHP1" (0.000100)
1034. feature "SELENOH" (0.000100)
1035. feature "UCA1" (0.000100)
1036. feature "LDLRAP1" (0.000100)
1037. feature "KRT81" (0.000100)
1038. feature "KCNK6" (0.000100)
1039. feature "PHC3" (0.000099)
1040. feature "MYEF2" (0.000099)
1041. feature "ZDHHC6" (0.000099)
1042. feature "CCDC26" (0.000099)
1043. feature "HIPK2" (0.000099)
1044. feature "MTMR12" (0.000099)
1045. feature "PANK3" (0.000099)
1046. feature "POGK" (0.000099)
1047. feature "MLLT6" (0.000098)
1048. feature "WDR54" (0.000098)
1049. feature "WDR82" (0.000098)
1050. feature "SPRR1B" (0.000098)
1051. feature "CNOT2" (0.000098)
1052. feature "SLC35C2" (0.000098)
1053. feature "MIR210HG" (0.000098)
1054. feature "EPS8L2" (0.000098)
1055. feature "LSM14A" (0.000098)
1056. feature "RHBDD2" (0.000098)
1057. feature "KCTD3" (0.000098)
1058. feature "SMC5" (0.000097)
1059. feature "GDE1" (0.000097)
1060. feature "RNF4" (0.000097)
1061. feature "UBE2I" (0.000097)
1062. feature "DHX40" (0.000097)
1063. feature "PCF11" (0.000097)
1064. feature "ARFGEF3" (0.000097)
1065. feature "TNS4" (0.000097)
1066. feature "HMGCS1" (0.000097)
1067. feature "LYPD4" (0.000097)
1068. feature "NEDD1" (0.000097)
1069. feature "GNG5" (0.000097)
1070. feature "TMEM45B" (0.000096)
1071. feature "RAB1B" (0.000096)
1072. feature "OTULIN" (0.000096)
1073. feature "CA2" (0.000096)
1074. feature "MPHOSPH9" (0.000096)
1075. feature "FBXW2" (0.000096)
1076. feature "ARID1A" (0.000095)
1077. feature "MKNK2" (0.000095)
1078. feature "BRCC3" (0.000095)
1079. feature "SGO1" (0.000095)
1080. feature "NOM1" (0.000095)
1081. feature "MUC5B" (0.000094)
1082. feature "PPP2R2C" (0.000094)
1083. feature "BAG1" (0.000094)
1084. feature "ARAF" (0.000094)
1085. feature "PPARA" (0.000094)
1086. feature "RRM1" (0.000094)
1087. feature "PDE8A" (0.000094)
1088. feature "WWC3" (0.000094)
1089. feature "STT3A" (0.000093)
1090. feature "ETAA1" (0.000093)
1091. feature "RHNO1" (0.000093)
1092. feature "CREB3L2" (0.000093)
1093. feature "ZFAS1" (0.000093)
1094. feature "TNFRSF12A" (0.000093)
1095. feature "KLF6" (0.000093)
1096. feature "TMEM45A" (0.000093)
1097. feature "RAP1A" (0.000092)
1098. feature "SLC35A4" (0.000092)
1099. feature "DHX16" (0.000092)
1100. feature "ETNK1" (0.000092)
1101. feature "FAM126B" (0.000092)
1102. feature "MED29" (0.000092)
1103. feature "ZNF629" (0.000092)
1104. feature "NAB1" (0.000092)
1105. feature "IFI6" (0.000092)
1106. feature "SRSF1" (0.000092)
1107. feature "PIP4K2A" (0.000091)
1108. feature "GNL1" (0.000091)
1109. feature "ZNF512B" (0.000091)
1110. feature "CIP2A" (0.000091)
1111. feature "MAPK1IP1L" (0.000091)
1112. feature "CAV2" (0.000091)
1113. feature "PRR14L" (0.000091)
1114. feature "VMA21" (0.000091)
1115. feature "LINC02511" (0.000091)
1116. feature "NUDT14" (0.000091)
1117. feature "PARD3" (0.000091)
1118. feature "CEP250" (0.000091)
1119. feature "CDCA4" (0.000091)
1120. feature "MPP7" (0.000090)
1121. feature "POLE" (0.000090)
1122. feature "SOX11" (0.000090)
1123. feature "FRY" (0.000090)
1124. feature "ARHGAP12" (0.000090)
1125. feature "CHAMP1" (0.000090)
1126. feature "ANGPTL4" (0.000090)
1127. feature "SLC1A5" (0.000090)
1128. feature "PALB2" (0.000090)
1129. feature "UACA" (0.000090)
1130. feature "KIF16B" (0.000089)
1131. feature "OPA1" (0.000089)
1132. feature "PIAS2" (0.000089)
1133. feature "ARID1B" (0.000089)
1134. feature "TMEM164" (0.000089)
1135. feature "AATF" (0.000089)
1136. feature "KCTD11" (0.000089)
1137. feature "FBXL19" (0.000089)
1138. feature "TEX261" (0.000089)
1139. feature "EIF5AL1" (0.000089)
1140. feature "RAB5C" (0.000089)
1141. feature "CDT1" (0.000089)
1142. feature "KIF13A" (0.000089)
1143. feature "DAXX" (0.000088)
1144. feature "SNHG6" (0.000088)
1145. feature "CHMP1A" (0.000088)
1146. feature "EPN1" (0.000088)
1147. feature "RARG" (0.000088)
1148. feature "KPNA3" (0.000088)
1149. feature "MARVELD3" (0.000088)
1150. feature "HIP1" (0.000088)
1151. feature "REV1" (0.000088)
1152. feature "RBM14" (0.000087)
1153. feature "KLHL7" (0.000087)
1154. feature "PCDHGA10" (0.000087)
1155. feature "IFITM2" (0.000087)
1156. feature "METTL5" (0.000087)
1157. feature "KRT15" (0.000087)
1158. feature "JOSD2" (0.000087)
1159. feature "OPRM1" (0.000087)
1160. feature "TTC28" (0.000087)
1161. feature "TATDN2" (0.000087)
1162. feature "SAP130" (0.000087)
1163. feature "KLC2" (0.000086)
1164. feature "KDM1B" (0.000086)
1165. feature "SAPCD2" (0.000086)
1166. feature "SRA1" (0.000086)
1167. feature "ERBIN" (0.000086)
1168. feature "MXRA5" (0.000086)
1169. feature "DHX38" (0.000086)
1170. feature "MACC1" (0.000086)
1171. feature "MFN2" (0.000086)
1172. feature "DLD" (0.000085)
1173. feature "EIPR1" (0.000085)
1174. feature "MYO9A" (0.000085)
1175. feature "PCBP1" (0.000085)
1176. feature "PSMD3" (0.000085)
1177. feature "PYCARD" (0.000085)
1178. feature "FAM114A1" (0.000085)
1179. feature "LAD1" (0.000085)
1180. feature "RPGRIP1L" (0.000085)
1181. feature "SCYL2" (0.000085)
1182. feature "EFCAB2" (0.000085)
1183. feature "DEDD" (0.000085)
1184. feature "TIAL1" (0.000085)
1185. feature "ZC3H4" (0.000085)
1186. feature "SMIM27" (0.000084)
1187. feature "TMEM52B" (0.000084)
1188. feature "PLEKHA4" (0.000084)
1189. feature "FLOT1" (0.000084)
1190. feature "METRNL" (0.000084)
1191. feature "DIO2" (0.000084)
1192. feature "BIK" (0.000084)
1193. feature "TCHP" (0.000083)
1194. feature "SYTL4" (0.000083)
1195. feature "CLOCK" (0.000083)
1196. feature "EPC2" (0.000083)
1197. feature "RAB3GAP1" (0.000083)
1198. feature "MED25" (0.000083)
1199. feature "MT-TS2" (0.000083)
1200. feature "ERRFI1" (0.000083)
1201. feature "ZBTB10" (0.000083)
1202. feature "GADD45B" (0.000083)
1203. feature "PPIH" (0.000083)
1204. feature "PLCB4" (0.000083)
1205. feature "EID1" (0.000083)
1206. feature "NACC2" (0.000083)
1207. feature "EFNB2" (0.000083)
1208. feature "FNIP1" (0.000083)
1209. feature "PSMD5" (0.000083)
1210. feature "MAGI2-AS3" (0.000083)
1211. feature "GSR" (0.000082)
1212. feature "THEM4" (0.000082)
1213. feature "VPS4A" (0.000082)
1214. feature "SMIM1" (0.000082)
1215. feature "ZNF511" (0.000082)
1216. feature "NSMF" (0.000082)
1217. feature "SNAPC1" (0.000082)
1218. feature "CCNQ" (0.000082)
1219. feature "HMGB1P20" (0.000082)
1220. feature "NDUFC1" (0.000082)
1221. feature "ZNF385A" (0.000082)
1222. feature "SPDEF" (0.000082)
1223. feature "KCTD15" (0.000081)
1224. feature "POLR3A" (0.000081)
1225. feature "NECTIN1" (0.000081)
1226. feature "LINC00205" (0.000081)
1227. feature "LAMTOR2" (0.000081)
1228. feature "PRKX" (0.000081)
1229. feature "MELK" (0.000081)
1230. feature "CS" (0.000081)
1231. feature "AVPI1" (0.000081)
1232. feature "AMFR" (0.000081)
1233. feature "SRP68" (0.000081)
1234. feature "ROGDI" (0.000081)
1235. feature "ANXA3" (0.000080)
1236. feature "LRIF1" (0.000080)
1237. feature "CACNG4" (0.000080)
1238. feature "PGM1" (0.000080)
1239. feature "ANKRD26" (0.000080)
1240. feature "KCNJ15" (0.000080)
1241. feature "ENO2" (0.000080)
1242. feature "ARID3A" (0.000080)
1243. feature "ZFAND2A" (0.000080)
1244. feature "SCYL3" (0.000080)
1245. feature "WDR5" (0.000080)
1246. feature "FAM102B" (0.000080)
1247. feature "DVL3" (0.000080)
1248. feature "DBT" (0.000080)
1249. feature "NOP10" (0.000080)
1250. feature "PPP2R1A" (0.000079)
1251. feature "PPL" (0.000079)
1252. feature "MRPS6" (0.000079)
1253. feature "ARMC6" (0.000079)
1254. feature "RNF130" (0.000079)
1255. feature "ASNSD1" (0.000079)
1256. feature "IFT74" (0.000079)
1257. feature "AGRN" (0.000079)
1258. feature "NUMA1" (0.000079)
1259. feature "SYCP2" (0.000079)
1260. feature "DUSP23" (0.000078)
1261. feature "TOLLIP" (0.000078)
1262. feature "OTUD7B" (0.000078)
1263. feature "STRN4" (0.000078)
1264. feature "PRPF31" (0.000078)
1265. feature "RIMKLA" (0.000078)
1266. feature "LINC01234" (0.000078)
1267. feature "IPO4" (0.000078)
1268. feature "UNC45A" (0.000078)
1269. feature "SFMBT1" (0.000078)
1270. feature "DROSHA" (0.000078)
1271. feature "RAB29" (0.000078)
1272. feature "RIN2" (0.000078)
1273. feature "ATF5" (0.000078)
1274. feature "SLC25A5" (0.000078)
1275. feature "MIEN1" (0.000077)
1276. feature "GYG1" (0.000077)
1277. feature "MARK1" (0.000077)
1278. feature "PTOV1" (0.000077)
1279. feature "TBC1D9" (0.000077)
1280. feature "TMEM184C" (0.000077)
1281. feature "MAEA" (0.000077)
1282. feature "MTUS1" (0.000077)
1283. feature "CNOT3" (0.000077)
1284. feature "RECQL4" (0.000077)
1285. feature "OSBPL3" (0.000077)
1286. feature "FAM76B" (0.000077)
1287. feature "DKK1" (0.000077)
1288. feature "MCM5" (0.000077)
1289. feature "AFAP1L2" (0.000077)
1290. feature "CLK2" (0.000077)
1291. feature "GRK2" (0.000077)
1292. feature "ZCCHC2" (0.000077)
1293. feature "TACC2" (0.000076)
1294. feature "EIF1AD" (0.000076)
1295. feature "ADARB1" (0.000076)
1296. feature "SPG21" (0.000076)
1297. feature "PPP1R21" (0.000076)
1298. feature "AGAP1" (0.000076)
1299. feature "FLAD1" (0.000076)
1300. feature "IPO9" (0.000076)
1301. feature "MAP2K3" (0.000076)
1302. feature "GNAI2" (0.000076)
1303. feature "GDAP2" (0.000076)
1304. feature "TAF9B" (0.000076)
1305. feature "NCAM1" (0.000076)
1306. feature "KRT4" (0.000076)
1307. feature "CSKMT" (0.000076)
1308. feature "FBRSL1" (0.000076)
1309. feature "SNAPC5" (0.000076)
1310. feature "AZI2" (0.000076)
1311. feature "MYB" (0.000076)
1312. feature "TMSB4XP4" (0.000075)
1313. feature "NUP93" (0.000075)
1314. feature "ARHGAP11A" (0.000075)
1315. feature "RGS10" (0.000075)
1316. feature "NACA4P" (0.000075)
1317. feature "ZNF85" (0.000075)
1318. feature "SECISBP2L" (0.000075)
1319. feature "DOCK10" (0.000075)
1320. feature "PATJ" (0.000075)
1321. feature "PMVK" (0.000075)
1322. feature "CXCL12" (0.000075)
1323. feature "PPP1R13L" (0.000075)
1324. feature "MRFAP1" (0.000075)
1325. feature "HGH1" (0.000075)
1326. feature "BAIAP2-DT" (0.000074)
1327. feature "PLCD3" (0.000074)
1328. feature "ITPR3" (0.000074)
1329. feature "ZHX3" (0.000074)
1330. feature "STX3" (0.000074)
1331. feature "KLHL8" (0.000074)
1332. feature "ARHGAP21" (0.000074)
1333. feature "REXO1" (0.000074)
1334. feature "MAP7D1" (0.000074)
1335. feature "RAB5A" (0.000074)
1336. feature "RFWD3" (0.000074)
1337. feature "PIN4" (0.000073)
1338. feature "BRWD3" (0.000073)
1339. feature "GLE1" (0.000073)
1340. feature "FAM13B" (0.000073)
1341. feature "SERINC5" (0.000073)
1342. feature "CMPK1" (0.000073)
1343. feature "NEK1" (0.000073)
1344. feature "RAB31" (0.000073)
1345. feature "NPY1R" (0.000073)
1346. feature "ZNF580" (0.000073)
1347. feature "PLSCR1" (0.000073)
1348. feature "BRMS1" (0.000073)
1349. feature "CTSL" (0.000073)
1350. feature "VCPIP1" (0.000072)
1351. feature "MYADM" (0.000072)
1352. feature "PGLS" (0.000072)
1353. feature "TYW3" (0.000072)
1354. feature "MKNK1" (0.000072)
1355. feature "TRIM7" (0.000072)
1356. feature "SLC2A4RG" (0.000072)
1357. feature "TRAF3IP1" (0.000072)
1358. feature "DCTN5" (0.000072)
1359. feature "PKP3" (0.000072)
1360. feature "TIAM1" (0.000072)
1361. feature "C11orf24" (0.000072)
1362. feature "SRFBP1" (0.000072)
1363. feature "IQCG" (0.000072)
1364. feature "ZNF26" (0.000072)
1365. feature "NDUFB4" (0.000072)
1366. feature "AFF1" (0.000072)
1367. feature "ELP3" (0.000072)
1368. feature "UBQLN4" (0.000071)
1369. feature "UPK1B" (0.000071)
1370. feature "PCGF3" (0.000071)
1371. feature "RUSC1" (0.000071)
1372. feature "BCL3" (0.000071)
1373. feature "HNRNPUL2" (0.000071)
1374. feature "FAM189B" (0.000071)
1375. feature "CASK" (0.000071)
1376. feature "CDC42EP1" (0.000071)
1377. feature "C1orf43" (0.000071)
1378. feature "ZNF268" (0.000070)
1379. feature "USP53" (0.000070)
1380. feature "TRIP13" (0.000070)
1381. feature "CARM1" (0.000070)
1382. feature "PMPCA" (0.000070)
1383. feature "WIPI2" (0.000070)
1384. feature "RAB3D" (0.000070)
1385. feature "KHNYN" (0.000070)
1386. feature "LIMS1" (0.000070)
1387. feature "BACH1" (0.000070)
1388. feature "B3GAT2" (0.000070)
1389. feature "MRPL45" (0.000070)
1390. feature "SPATA33" (0.000070)
1391. feature "MCM6" (0.000070)
1392. feature "NFX1" (0.000070)
1393. feature "YEATS4" (0.000070)
1394. feature "NCLN" (0.000070)
1395. feature "DLG5" (0.000070)
1396. feature "GADD45A" (0.000070)
1397. feature "UBE2E1" (0.000069)
1398. feature "LCMT1" (0.000069)
1399. feature "BRK1" (0.000069)
1400. feature "EHBP1L1" (0.000069)
1401. feature "PLAAT3" (0.000069)
1402. feature "PLEKHF2" (0.000069)
1403. feature "HES4" (0.000069)
1404. feature "DNPH1" (0.000069)
1405. feature "ORC4" (0.000069)
1406. feature "POLD2" (0.000069)
1407. feature "SPATA20" (0.000069)
1408. feature "BRPF3" (0.000069)
1409. feature "PITPNA" (0.000069)
1410. feature "PPP1R3D" (0.000069)
1411. feature "NECAB3" (0.000069)
1412. feature "RESF1" (0.000068)
1413. feature "TBC1D20" (0.000068)
1414. feature "AKR1A1" (0.000068)
1415. feature "WAC-AS1" (0.000068)
1416. feature "MGLL" (0.000068)
1417. feature "CEP128" (0.000068)
1418. feature "BTBD2" (0.000068)
1419. feature "POLR2F" (0.000068)
1420. feature "TOMM5" (0.000068)
1421. feature "GCLM" (0.000068)
1422. feature "TTC19" (0.000068)
1423. feature "MIPOL1" (0.000068)
1424. feature "CHTOP" (0.000068)
1425. feature "MAD2L2" (0.000067)
1426. feature "ADORA2B" (0.000067)
1427. feature "TNKS1BP1" (0.000067)
1428. feature "CHN1" (0.000067)
1429. feature "C1orf21" (0.000067)
1430. feature "CHAF1A" (0.000067)
1431. feature "FOXJ2" (0.000067)
1432. feature "PLAC8" (0.000067)
1433. feature "ZDHHC7" (0.000067)
1434. feature "BMPR2" (0.000067)
1435. feature "TARDBP" (0.000067)
1436. feature "YARS1" (0.000067)
1437. feature "SPINT1-AS1" (0.000067)
1438. feature "CLDN11" (0.000066)
1439. feature "SLC26A4-AS1" (0.000066)
1440. feature "RNF187" (0.000066)
1441. feature "PFKL" (0.000066)
1442. feature "CCP110" (0.000066)
1443. feature "HERC1" (0.000066)
1444. feature "COG4" (0.000066)
1445. feature "PHKG2" (0.000066)
1446. feature "CAMSAP1" (0.000066)
1447. feature "MCM2" (0.000066)
1448. feature "ZFHX3" (0.000065)
1449. feature "CBFA2T3" (0.000065)
1450. feature "VRK2" (0.000065)
1451. feature "HOOK1" (0.000065)
1452. feature "TMEM70" (0.000065)
1453. feature "MAILR" (0.000065)
1454. feature "MYO1C" (0.000065)
1455. feature "ADAMTS19" (0.000065)
1456. feature "DPM3" (0.000065)
1457. feature "FARP1" (0.000065)
1458. feature "C5orf51" (0.000065)
1459. feature "QRSL1" (0.000065)
1460. feature "SNCG" (0.000065)
1461. feature "KLC1" (0.000065)
1462. feature "JUND" (0.000065)
1463. feature "WWC1" (0.000065)
1464. feature "FBRS" (0.000065)
1465. feature "IER5" (0.000065)
1466. feature "COMT" (0.000064)
1467. feature "ARID4A" (0.000064)
1468. feature "PPP2R5D" (0.000064)
1469. feature "SIX4" (0.000064)
1470. feature "ZBTB2" (0.000064)
1471. feature "SIAH2" (0.000064)
1472. feature "CDR2L" (0.000064)
1473. feature "FUOM" (0.000064)
1474. feature "ATP13A2" (0.000064)
1475. feature "E2F4" (0.000064)
1476. feature "RC3H2" (0.000064)
1477. feature "ABL2" (0.000064)
1478. feature "UBE2J1" (0.000064)
1479. feature "EXOC7" (0.000064)
1480. feature "ZNF354A" (0.000064)
1481. feature "XRCC2" (0.000064)
1482. feature "CTBP1" (0.000064)
1483. feature "GRIPAP1" (0.000063)
1484. feature "SMG5" (0.000063)
1485. feature "SLC48A1" (0.000063)
1486. feature "FAM181B" (0.000063)
1487. feature "XPNPEP1" (0.000063)
1488. feature "SCCPDH" (0.000063)
1489. feature "POLK" (0.000063)
1490. feature "MDFI" (0.000063)
1491. feature "ZNF165" (0.000063)
1492. feature "TNFAIP8L1" (0.000063)
1493. feature "SLC2A11" (0.000063)
1494. feature "TGFB3" (0.000063)
1495. feature "RAB27A" (0.000063)
1496. feature "AKR7A2" (0.000063)
1497. feature "COL7A1" (0.000063)
1498. feature "CEP120" (0.000063)
1499. feature "SLC52A2" (0.000063)
1500. feature "ZNF252P" (0.000063)
1501. feature "ZADH2" (0.000063)
1502. feature "GMPPA" (0.000063)
1503. feature "DGKD" (0.000062)
1504. feature "PSMA5" (0.000062)
1505. feature "AFAP1" (0.000062)
1506. feature "MAB21L3" (0.000062)
1507. feature "MLXIP" (0.000062)
1508. feature "ZNF8-DT" (0.000062)
1509. feature "ABT1" (0.000062)
1510. feature "DYNC2I1" (0.000062)
1511. feature "SDC1" (0.000062)
1512. feature "USP6NL" (0.000062)
1513. feature "PCDHB1" (0.000062)
1514. feature "IER3IP1" (0.000062)
1515. feature "LTBR" (0.000062)
1516. feature "EFCAB14" (0.000062)
1517. feature "RAB2B" (0.000062)
1518. feature "AP1S3" (0.000062)
1519. feature "AXIN1" (0.000062)
1520. feature "HMGB1P8" (0.000062)
1521. feature "SPP1" (0.000062)
1522. feature "PIK3CB" (0.000061)
1523. feature "PLCE1" (0.000061)
1524. feature "WDR26" (0.000061)
1525. feature "DDIAS" (0.000061)
1526. feature "PCCA-DT" (0.000061)
1527. feature "FAM210B" (0.000061)
1528. feature "DNAJA3" (0.000061)
1529. feature "MAPK9" (0.000061)
1530. feature "MED1" (0.000061)
1531. feature "CPEB1" (0.000061)
1532. feature "CREB1" (0.000061)
1533. feature "SSNA1" (0.000061)
1534. feature "FOXO3" (0.000061)
1535. feature "SLC19A1" (0.000061)
1536. feature "DIP2C" (0.000061)
1537. feature "ZNF358" (0.000061)
1538. feature "DUS4L" (0.000061)
1539. feature "BACE2" (0.000060)
1540. feature "NKAPD1" (0.000060)
1541. feature "ZNF91" (0.000060)
1542. feature "MARVELD1" (0.000060)
1543. feature "CCDC57" (0.000060)
1544. feature "HOMER2" (0.000060)
1545. feature "IER5L" (0.000060)
1546. feature "MSMB" (0.000060)
1547. feature "SOS2" (0.000060)
1548. feature "TMCC1" (0.000060)
1549. feature "UHRF1BP1" (0.000060)
1550. feature "AGO1" (0.000060)
1551. feature "NSMCE4A" (0.000060)
1552. feature "ZMYM3" (0.000060)
1553. feature "FOXC1" (0.000060)
1554. feature "ZNF280B" (0.000060)
1555. feature "PUSL1" (0.000060)
1556. feature "APEH" (0.000060)
1557. feature "BRMS1L" (0.000060)
1558. feature "TNNT1" (0.000060)
1559. feature "NFKB1" (0.000060)
1560. feature "TST" (0.000060)
1561. feature "HAUS1" (0.000060)
1562. feature "BTBD6" (0.000059)
1563. feature "SPIRE1" (0.000059)
1564. feature "UBE2E3" (0.000059)
1565. feature "TCEAL3" (0.000059)
1566. feature "ASB8" (0.000059)
1567. feature "LIMK1" (0.000059)
1568. feature "FBXO42" (0.000059)
1569. feature "MT-TD" (0.000059)
1570. feature "LYPLA2" (0.000059)
1571. feature "YJEFN3" (0.000059)
1572. feature "KLF9" (0.000059)
1573. feature "PCIF1" (0.000059)
1574. feature "TUBB6" (0.000059)
1575. feature "TMEM80" (0.000059)
1576. feature "CHCHD7" (0.000059)
1577. feature "LDHB" (0.000059)
1578. feature "CENPM" (0.000059)
1579. feature "SELENOO" (0.000059)
1580. feature "CAMKK2" (0.000059)
1581. feature "PLK1" (0.000059)
1582. feature "TEDC2-AS1" (0.000059)
1583. feature "TRIB3" (0.000059)
1584. feature "GMEB2" (0.000059)
1585. feature "SNX4" (0.000059)
1586. feature "ARHGAP23" (0.000059)
1587. feature "AJAP1" (0.000058)
1588. feature "DCLRE1B" (0.000058)
1589. feature "WDR46" (0.000058)
1590. feature "ENOX2" (0.000058)
1591. feature "CCNC" (0.000058)
1592. feature "DANT1" (0.000058)
1593. feature "SIPA1L1" (0.000058)
1594. feature "PET117" (0.000058)
1595. feature "FASTKD3" (0.000058)
1596. feature "ARHGEF7" (0.000058)
1597. feature "EVPL" (0.000058)
1598. feature "POLD1" (0.000058)
1599. feature "TCF3" (0.000058)
1600. feature "NOL4L" (0.000058)
1601. feature "FAM193B" (0.000058)
1602. feature "PHACTR1" (0.000058)
1603. feature "ARAP1" (0.000058)
1604. feature "NGRN" (0.000058)
1605. feature "CACNB3" (0.000058)
1606. feature "RNASEH1" (0.000058)
1607. feature "SREBF1" (0.000058)
1608. feature "RNF32" (0.000058)
1609. feature "TSSC4" (0.000057)
1610. feature "ZFYVE9" (0.000057)
1611. feature "PKN1" (0.000057)
1612. feature "TAF12" (0.000057)
1613. feature "HCG18" (0.000057)
1614. feature "C2orf68" (0.000057)
1615. feature "RIPK1" (0.000057)
1616. feature "C1orf115" (0.000057)
1617. feature "LCORL" (0.000057)
1618. feature "CAD" (0.000057)
1619. feature "IGFBP7" (0.000057)
1620. feature "SGTA" (0.000057)
1621. feature "SSX2IP" (0.000057)
1622. feature "SMIM30" (0.000057)
1623. feature "WDR18" (0.000057)
1624. feature "ECH1" (0.000057)
1625. feature "HMG20B" (0.000057)
1626. feature "MCRIP1" (0.000057)
1627. feature "CRPPA" (0.000057)
1628. feature "GNPAT" (0.000057)
1629. feature "RAB30" (0.000057)
1630. feature "VARS1" (0.000057)
1631. feature "SPRYD4" (0.000057)
1632. feature "GIT1" (0.000057)
1633. feature "RFC5" (0.000057)
1634. feature "CDK9" (0.000056)
1635. feature "TRMT2A" (0.000056)
1636. feature "MIR99AHG" (0.000056)
1637. feature "PRDM4" (0.000056)
1638. feature "BIRC5" (0.000056)
1639. feature "MT-TC" (0.000056)
1640. feature "SH3BGRL" (0.000056)
1641. feature "VPS26B" (0.000056)
1642. feature "PYCR1" (0.000056)
1643. feature "ABLIM1" (0.000056)
1644. feature "SLAIN2" (0.000056)
1645. feature "SURF1" (0.000056)
1646. feature "L2HGDH" (0.000056)
1647. feature "PACSIN3" (0.000056)
1648. feature "CRBN" (0.000056)
1649. feature "PRPSAP2" (0.000056)
1650. feature "KDM4B" (0.000056)
1651. feature "SCARB1" (0.000056)
1652. feature "SPAG1" (0.000056)
1653. feature "KIF9" (0.000056)
1654. feature "CLEC4OP" (0.000056)
1655. feature "HS6ST1" (0.000056)
1656. feature "KLLN" (0.000056)
1657. feature "ARHGAP17" (0.000056)
1658. feature "AKT2" (0.000056)
1659. feature "ALDOA" (0.000056)
1660. feature "SLC25A51" (0.000055)
1661. feature "CSNK2A2" (0.000055)
1662. feature "SLC25A28" (0.000055)
1663. feature "PFKFB4" (0.000055)
1664. feature "PSMG1" (0.000055)
1665. feature "FOXRED2" (0.000055)
1666. feature "FADS3" (0.000055)
1667. feature "HOXC13" (0.000055)
1668. feature "UBL4A" (0.000055)
1669. feature "BRF1" (0.000055)
1670. feature "CYP1A1" (0.000055)
1671. feature "MGAT4A" (0.000055)
1672. feature "CNDP2" (0.000055)
1673. feature "ANKMY2" (0.000055)
1674. feature "MLLT10" (0.000055)
1675. feature "ZZZ3" (0.000055)
1676. feature "S1PR2" (0.000055)
1677. feature "CCS" (0.000055)
1678. feature "TP53INP1" (0.000055)
1679. feature "FBXO30" (0.000055)
1680. feature "KXD1" (0.000054)
1681. feature "FNTA" (0.000054)
1682. feature "MROH1" (0.000054)
1683. feature "TMEM256" (0.000054)
1684. feature "NEIL3" (0.000054)
1685. feature "C5orf38" (0.000054)
1686. feature "ZNF574" (0.000054)
1687. feature "IRS2" (0.000054)
1688. feature "BICD2" (0.000054)
1689. feature "LMX1B" (0.000054)
1690. feature "TBC1D10A" (0.000054)
1691. feature "ZDHHC12" (0.000054)
1692. feature "ZNF398" (0.000054)
1693. feature "BTRC" (0.000054)
1694. feature "SNORD3B-1" (0.000054)
1695. feature "LAMA5" (0.000054)
1696. feature "HSPA2" (0.000054)
1697. feature "CKB" (0.000054)
1698. feature "COL6A3" (0.000054)
1699. feature "BEND6" (0.000054)
1700. feature "RRP12" (0.000054)
1701. feature "SETD1B" (0.000054)
1702. feature "TEAD1" (0.000054)
1703. feature "MT-TN" (0.000054)
1704. feature "SRF" (0.000054)
1705. feature "ZBTB34" (0.000054)
1706. feature "LRRC45" (0.000054)
1707. feature "ARHGEF28" (0.000054)
1708. feature "SHKBP1" (0.000053)
1709. feature "PHRF1" (0.000053)
1710. feature "OTUD4" (0.000053)
1711. feature "CKS1B" (0.000053)
1712. feature "SDHAF2" (0.000053)
1713. feature "ITSN2" (0.000053)
1714. feature "SORBS3" (0.000053)
1715. feature "AP2A1" (0.000053)
1716. feature "DOHH" (0.000053)
1717. feature "ABL1" (0.000053)
1718. feature "RAI14" (0.000053)
1719. feature "ZNFX1" (0.000053)
1720. feature "AKAP5" (0.000053)
1721. feature "FCHO2" (0.000053)
1722. feature "TPGS1" (0.000053)
1723. feature "RNF126" (0.000053)
1724. feature "NPRL3" (0.000053)
1725. feature "NF2" (0.000053)
1726. feature "SAMD12" (0.000053)
1727. feature "TCF7L1" (0.000053)
1728. feature "SF3B4" (0.000053)
1729. feature "SOX15" (0.000053)
1730. feature "NCK1-DT" (0.000053)
1731. feature "CPTP" (0.000053)
1732. feature "SMKR1" (0.000052)
1733. feature "DCP1A" (0.000052)
1734. feature "PIGS" (0.000052)
1735. feature "PPP1R37" (0.000052)
1736. feature "CABLES1" (0.000052)
1737. feature "CTPS2" (0.000052)
1738. feature "ID3" (0.000052)
1739. feature "USP38" (0.000052)
1740. feature "NOL3" (0.000052)
1741. feature "MAP3K14" (0.000052)
1742. feature "ZNF598" (0.000052)
1743. feature "KSR2" (0.000052)
1744. feature "GTPBP6" (0.000052)
1745. feature "TTYH3" (0.000052)
1746. feature "PAK4" (0.000052)
1747. feature "PFKFB2" (0.000052)
1748. feature "SETBP1" (0.000052)
1749. feature "WBP2" (0.000052)
1750. feature "MGAT4B" (0.000052)
1751. feature "UBXN2B" (0.000052)
1752. feature "KCTD2" (0.000052)
1753. feature "RGL2" (0.000052)
1754. feature "CEP135" (0.000052)
1755. feature "TFRC" (0.000052)
1756. feature "TPRA1" (0.000052)
1757. feature "WT1-AS" (0.000052)
1758. feature "TBL1X" (0.000052)
1759. feature "VPS54" (0.000052)
1760. feature "MAP2" (0.000051)
1761. feature "ANKRD39" (0.000051)
1762. feature "FANCE" (0.000051)
1763. feature "POLE2" (0.000051)
1764. feature "PCNX2" (0.000051)
1765. feature "MINDY2" (0.000051)
1766. feature "BNC1" (0.000051)
1767. feature "ZBTB37" (0.000051)
1768. feature "SPA17" (0.000051)
1769. feature "TMEM250" (0.000051)
1770. feature "HHLA3" (0.000051)
1771. feature "METTL22" (0.000051)
1772. feature "CCDC8" (0.000051)
1773. feature "ERI1" (0.000051)
1774. feature "RHBDL2" (0.000051)
1775. feature "TMEM259" (0.000051)
1776. feature "NR1H2" (0.000051)
1777. feature "CLDN4" (0.000051)
1778. feature "FARP2" (0.000051)
1779. feature "CANT1" (0.000051)
1780. feature "PLA2G12A" (0.000050)
1781. feature "L3MBTL2" (0.000050)
1782. feature "YAF2" (0.000050)
1783. feature "HPCAL1" (0.000050)
1784. feature "ARL2" (0.000050)
1785. feature "SIRT1" (0.000050)
1786. feature "AKAP17A" (0.000050)
1787. feature "ARNTL2" (0.000050)
1788. feature "NCOA2" (0.000050)
1789. feature "KMT2B" (0.000050)
1790. feature "FBXO11" (0.000050)
1791. feature "C8orf82" (0.000050)
1792. feature "GCLC" (0.000050)
1793. feature "BOP1" (0.000050)
1794. feature "COL5A1" (0.000050)
1795. feature "NCK1" (0.000050)
1796. feature "SAMD15" (0.000050)
1797. feature "PTPN13" (0.000050)
1798. feature "WDR77" (0.000050)
1799. feature "SENP3" (0.000050)
1800. feature "SSH3" (0.000050)
1801. feature "MBD3" (0.000050)
1802. feature "HOMER3" (0.000050)
1803. feature "DCAF10" (0.000050)
1804. feature "ZNF483" (0.000049)
1805. feature "R3HCC1L" (0.000049)
1806. feature "TCEAL1" (0.000049)
1807. feature "NPLOC4" (0.000049)
1808. feature "CHTF18" (0.000049)
1809. feature "MLX" (0.000049)
1810. feature "DBNDD1" (0.000049)
1811. feature "CNTROB" (0.000049)
1812. feature "ALDOC" (0.000049)
1813. feature "ADAT1" (0.000049)
1814. feature "ZNF628" (0.000049)
1815. feature "PPP2R5A" (0.000049)
1816. feature "RNF220" (0.000049)
1817. feature "PCYT1B" (0.000049)
1818. feature "TRIM41" (0.000049)
1819. feature "SNX24" (0.000049)
1820. feature "ESS2" (0.000049)
1821. feature "EZH1" (0.000049)
1822. feature "RRN3P3" (0.000049)
1823. feature "PATL1" (0.000049)
1824. feature "LSM10" (0.000049)
1825. feature "NLE1" (0.000049)
1826. feature "EGR3" (0.000049)
1827. feature "GMFB" (0.000048)
1828. feature "ZNF362" (0.000048)
1829. feature "TOP1MT" (0.000048)
1830. feature "SCAF8" (0.000048)
1831. feature "ZNF142" (0.000048)
1832. feature "SCPEP1" (0.000048)
1833. feature "ZNF777" (0.000048)
1834. feature "DRAM2" (0.000048)
1835. feature "PAN3" (0.000048)
1836. feature "PSMG3-AS1" (0.000048)
1837. feature "TET3" (0.000048)
1838. feature "TONSL" (0.000048)
1839. feature "BCL2L12" (0.000048)
1840. feature "MORC3" (0.000048)
1841. feature "LIAS" (0.000048)
1842. feature "C16orf91" (0.000048)
1843. feature "CUL4B" (0.000048)
1844. feature "CTSZ" (0.000048)
1845. feature "QSOX1" (0.000048)
1846. feature "PICALM" (0.000048)
1847. feature "POLR2H" (0.000048)
1848. feature "DYRK2" (0.000048)
1849. feature "AMOTL2" (0.000048)
1850. feature "ANXA4" (0.000048)
1851. feature "ACKR3" (0.000048)
1852. feature "P2RY2" (0.000048)
1853. feature "CIPC" (0.000048)
1854. feature "NAGK" (0.000048)
1855. feature "EFNB1" (0.000048)
1856. feature "CIAO2A" (0.000048)
1857. feature "ISY1" (0.000048)
1858. feature "PPM1A" (0.000047)
1859. feature "RNF223" (0.000047)
1860. feature "PATZ1" (0.000047)
1861. feature "CAMK2D" (0.000047)
1862. feature "CAMSAP3" (0.000047)
1863. feature "LINC02582" (0.000047)
1864. feature "CA9" (0.000047)
1865. feature "CRKL" (0.000047)
1866. feature "SSH1" (0.000047)
1867. feature "NUAK1" (0.000047)
1868. feature "HDAC7" (0.000047)
1869. feature "GTPBP2" (0.000047)
1870. feature "OGDH" (0.000047)
1871. feature "GABPA" (0.000047)
1872. feature "MNT" (0.000047)
1873. feature "RNF34" (0.000047)
1874. feature "ZNF605" (0.000047)
1875. feature "EFHD1" (0.000047)
1876. feature "SIPA1L2" (0.000047)
1877. feature "ZNF324" (0.000047)
1878. feature "DNAAF5" (0.000047)
1879. feature "SCAPER" (0.000047)
1880. feature "TMEM69" (0.000047)
1881. feature "AGAP3" (0.000047)
1882. feature "LYPD1" (0.000047)
1883. feature "SLC38A10" (0.000047)
1884. feature "AK7" (0.000047)
1885. feature "PNMA1" (0.000046)
1886. feature "CCND3" (0.000046)
1887. feature "TBC1D13" (0.000046)
1888. feature "TMEM40" (0.000046)
1889. feature "MUL1" (0.000046)
1890. feature "WFS1" (0.000046)
1891. feature "TRIM8" (0.000046)
1892. feature "IRX2" (0.000046)
1893. feature "RHOT2" (0.000046)
1894. feature "BRAT1" (0.000046)
1895. feature "ORAI1" (0.000046)
1896. feature "CASP6" (0.000046)
1897. feature "WDFY3" (0.000046)
1898. feature "JADE2" (0.000046)
1899. feature "TYMP" (0.000046)
1900. feature "CAPNS1" (0.000046)
1901. feature "GREM1" (0.000046)
1902. feature "SNAP47" (0.000046)
1903. feature "YY1AP1" (0.000046)
1904. feature "LINC01503" (0.000046)
1905. feature "IMP3" (0.000046)
1906. feature "NUP98" (0.000046)
1907. feature "RAB11FIP4" (0.000046)
1908. feature "ADGRG1" (0.000046)
1909. feature "NDUFA8" (0.000046)
1910. feature "FDFT1" (0.000046)
1911. feature "JMJD6" (0.000046)
1912. feature "FNDC10" (0.000046)
1913. feature "LMF2" (0.000046)
1914. feature "NEMP1" (0.000045)
1915. feature "GATC" (0.000045)
1916. feature "UBA3" (0.000045)
1917. feature "SP2" (0.000045)
1918. feature "SDHAF3" (0.000045)
1919. feature "STK39" (0.000045)
1920. feature "HAUS5" (0.000045)
1921. feature "FGD4" (0.000045)
1922. feature "ADM" (0.000045)
1923. feature "TIMM29" (0.000045)
1924. feature "USF1" (0.000045)
1925. feature "NIPSNAP3A" (0.000045)
1926. feature "TTLL5" (0.000045)
1927. feature "FRS2" (0.000045)
1928. feature "ZNF526" (0.000045)
1929. feature "FGFBP1" (0.000045)
1930. feature "ERVMER34-1" (0.000045)
1931. feature "SYK" (0.000045)
1932. feature "POLG" (0.000045)
1933. feature "PIP4P1" (0.000045)
1934. feature "PEA15" (0.000045)
1935. feature "TCAF1" (0.000045)
1936. feature "UBE2D1" (0.000044)
1937. feature "C22orf46" (0.000044)
1938. feature "TRAK1" (0.000044)
1939. feature "GFOD1" (0.000044)
1940. feature "DNAJC5" (0.000044)
1941. feature "PHF8" (0.000044)
1942. feature "GAS8" (0.000044)
1943. feature "SOCS2" (0.000044)
1944. feature "GPATCH1" (0.000044)
1945. feature "PPP4R1" (0.000044)
1946. feature "ABR" (0.000044)
1947. feature "TLE3" (0.000044)
1948. feature "SAMHD1" (0.000044)
1949. feature "USP25" (0.000044)
1950. feature "DERA" (0.000044)
1951. feature "CCNY" (0.000044)
1952. feature "KBTBD6" (0.000044)
1953. feature "SALL4" (0.000044)
1954. feature "PRPS1" (0.000044)
1955. feature "FAM83G" (0.000044)
1956. feature "RPL17" (0.000044)
1957. feature "MTMR2" (0.000044)
1958. feature "NXF1" (0.000044)
1959. feature "ZBED2" (0.000044)
1960. feature "CDK6" (0.000044)
1961. feature "EHD1" (0.000044)
1962. feature "LRRCC1" (0.000044)
1963. feature "SACS" (0.000044)
1964. feature "TP53BP2" (0.000044)
1965. feature "GATA2" (0.000044)
1966. feature "C6orf132" (0.000044)
1967. feature "C19orf47" (0.000044)
1968. feature "RTL6" (0.000044)
1969. feature "FAM104A" (0.000044)
1970. feature "LATS2" (0.000044)
1971. feature "NAALADL2" (0.000044)
1972. feature "MXD1" (0.000043)
1973. feature "LEMD3" (0.000043)
1974. feature "ACOT7" (0.000043)
1975. feature "RBAK" (0.000043)
1976. feature "TEX30" (0.000043)
1977. feature "HID1" (0.000043)
1978. feature "KAZN" (0.000043)
1979. feature "IRS1" (0.000043)
1980. feature "ZNF544" (0.000043)
1981. feature "WNT7B" (0.000043)
1982. feature "FYCO1" (0.000043)
1983. feature "TAF6" (0.000043)
1984. feature "CLIC4" (0.000043)
1985. feature "B3GALT6" (0.000043)
1986. feature "MPDU1" (0.000043)
1987. feature "ZBED4" (0.000043)
1988. feature "EHBP1" (0.000043)
1989. feature "TMEM184B" (0.000043)
1990. feature "TCHH" (0.000043)
1991. feature "MB" (0.000043)
1992. feature "HPRT1" (0.000043)
1993. feature "CRIP2" (0.000043)
1994. feature "FHL2" (0.000043)
1995. feature "CACNB2" (0.000043)
1996. feature "ZNF562" (0.000043)
1997. feature "MBNL3" (0.000043)
1998. feature "EP400P1" (0.000043)
1999. feature "FLOT2" (0.000043)
2000. feature "ZFAND2B" (0.000043)
2001. feature "SEPTIN8" (0.000043)
2002. feature "LINC02345" (0.000043)
2003. feature "OAZ2" (0.000043)
2004. feature "CNPPD1" (0.000043)
2005. feature "CHPF" (0.000043)
2006. feature "ZNF347" (0.000043)
2007. feature "ZRANB1" (0.000042)
2008. feature "PTPDC1" (0.000042)
2009. feature "GM2A" (0.000042)
2010. feature "PDE12" (0.000042)
2011. feature "CERS6" (0.000042)
2012. feature "SDHB" (0.000042)
2013. feature "GAS2L1" (0.000042)
2014. feature "PAPSS1" (0.000042)
2015. feature "ZNF467" (0.000042)
2016. feature "UBE2T" (0.000042)
2017. feature "C16orf72" (0.000042)
2018. feature "SIX3" (0.000042)
2019. feature "MIEF1" (0.000042)
2020. feature "LDOC1" (0.000042)
2021. feature "RHOV" (0.000042)
2022. feature "CMBL" (0.000042)
2023. feature "ZC3H12A" (0.000042)
2024. feature "NLK" (0.000042)
2025. feature "GRK6" (0.000042)
2026. feature "MREG" (0.000041)
2027. feature "DAZAP2" (0.000041)
2028. feature "ATXN1L" (0.000041)
2029. feature "MBD5" (0.000041)
2030. feature "MAP3K9" (0.000041)
2031. feature "TJP3" (0.000041)
2032. feature "UBE2F" (0.000041)
2033. feature "FRAT2" (0.000041)
2034. feature "MIXL1" (0.000041)
2035. feature "TCF19" (0.000041)
2036. feature "PXMP4" (0.000041)
2037. feature "DNAH11" (0.000041)
2038. feature "FAR1" (0.000041)
2039. feature "PLEKHG3" (0.000041)
2040. feature "MSL3P1" (0.000041)
2041. feature "PKMYT1" (0.000041)
2042. feature "CDIPT" (0.000041)
2043. feature "ABHD10" (0.000041)
2044. feature "CCDC88C" (0.000041)
2045. feature "CAPN10" (0.000041)
2046. feature "NPTN" (0.000041)
2047. feature "FAM83A-AS1" (0.000041)
2048. feature "OXNAD1" (0.000041)
2049. feature "KLHL20" (0.000041)
2050. feature "PITPNC1" (0.000041)
2051. feature "SFXN2" (0.000041)
2052. feature "PCNX3" (0.000041)
2053. feature "RAVER1" (0.000041)
2054. feature "INPPL1" (0.000041)
2055. feature "UBA6-DT" (0.000041)
2056. feature "RNPEPL1" (0.000040)
2057. feature "EPHB4" (0.000040)
2058. feature "SLC25A25-AS1" (0.000040)
2059. feature "SBNO2" (0.000040)
2060. feature "GMPR2" (0.000040)
2061. feature "ARHGEF18" (0.000040)
2062. feature "ARHGAP42" (0.000040)
2063. feature "RETREG2" (0.000040)
2064. feature "SRD5A1" (0.000040)
2065. feature "MASTL" (0.000040)
2066. feature "AVEN" (0.000040)
2067. feature "MT-TL1" (0.000040)
2068. feature "CTDSP2" (0.000040)
2069. feature "SLC25A46" (0.000040)
2070. feature "TTLL12" (0.000040)
2071. feature "CRK" (0.000040)
2072. feature "APPL1" (0.000040)
2073. feature "KRT16" (0.000040)
2074. feature "CBWD3" (0.000040)
2075. feature "PRR12" (0.000040)
2076. feature "VAMP7" (0.000040)
2077. feature "DCTN6-DT" (0.000040)
2078. feature "FBXW4" (0.000040)
2079. feature "PACS2" (0.000040)
2080. feature "VPS9D1-AS1" (0.000040)
2081. feature "ZNF8" (0.000040)
2082. feature "RIC8A" (0.000040)
2083. feature "TMEM39B" (0.000040)
2084. feature "CEP104" (0.000039)
2085. feature "NXPE3" (0.000039)
2086. feature "CHERP" (0.000039)
2087. feature "ZNF282" (0.000039)
2088. feature "AGBL5" (0.000039)
2089. feature "AKT1" (0.000039)
2090. feature "ODF2L" (0.000039)
2091. feature "SBF2" (0.000039)
2092. feature "CXXC5" (0.000039)
2093. feature "PRKCD" (0.000039)
2094. feature "SMIM26" (0.000039)
2095. feature "INF2" (0.000039)
2096. feature "KDM6B" (0.000039)
2097. feature "ZNF48" (0.000039)
2098. feature "BLOC1S3" (0.000039)
2099. feature "KIAA0753" (0.000039)
2100. feature "JPH1" (0.000039)
2101. feature "NADK" (0.000039)
2102. feature "KBTBD7" (0.000039)
2103. feature "ARHGEF11" (0.000039)
2104. feature "CLMN" (0.000039)
2105. feature "MAGEA3" (0.000038)
2106. feature "SMIM4" (0.000038)
2107. feature "PLP2" (0.000038)
2108. feature "GPR153" (0.000038)
2109. feature "CDK14" (0.000038)
2110. feature "CDC25B" (0.000038)
2111. feature "VPS52" (0.000038)
2112. feature "ZBTB26" (0.000038)
2113. feature "GLCCI1" (0.000038)
2114. feature "TRMT61A" (0.000038)
2115. feature "NMI" (0.000038)
2116. feature "THNSL1" (0.000038)
2117. feature "ARSA" (0.000038)
2118. feature "TPP1" (0.000038)
2119. feature "IDH3G" (0.000038)
2120. feature "SNHG18" (0.000038)
2121. feature "FOXK1" (0.000038)
2122. feature "IQANK1" (0.000038)
2123. feature "GTF3C5" (0.000038)
2124. feature "SMAD3" (0.000038)
2125. feature "HMBS" (0.000038)
2126. feature "MAGEA4" (0.000038)
2127. feature "BIN1" (0.000038)
2128. feature "ELF4" (0.000038)
2129. feature "HEATR3" (0.000038)
2130. feature "NKIRAS2" (0.000038)
2131. feature "UAP1L1" (0.000038)
2132. feature "EIF3F" (0.000038)
2133. feature "ANKRD13D" (0.000038)
2134. feature "LRRC73" (0.000038)
2135. feature "ZWILCH" (0.000037)
2136. feature "CNR2" (0.000037)
2137. feature "GLIS2" (0.000037)
2138. feature "MT1F" (0.000037)
2139. feature "CLUH" (0.000037)
2140. feature "DDX19A" (0.000037)
2141. feature "MAD2L1BP" (0.000037)
2142. feature "FTH1" (0.000037)
2143. feature "RHPN1" (0.000037)
2144. feature "AP1G2" (0.000037)
2145. feature "SNHG30" (0.000037)
2146. feature "SESN2" (0.000037)
2147. feature "SLC25A44" (0.000037)
2148. feature "PREB" (0.000037)
2149. feature "GAREM1" (0.000037)
2150. feature "ATP5MC2" (0.000037)
2151. feature "PTPRE" (0.000037)
2152. feature "ZDHHC5" (0.000037)
2153. feature "HEY1" (0.000037)
2154. feature "TXNRD2" (0.000037)
2155. feature "WDR62" (0.000037)
2156. feature "AIP" (0.000037)
2157. feature "PLEKHH3" (0.000037)
2158. feature "USP45" (0.000037)
2159. feature "METTL21A" (0.000037)
2160. feature "ZNF444" (0.000037)
2161. feature "INTS3" (0.000037)
2162. feature "PITPNM1" (0.000037)
2163. feature "FTO" (0.000037)
2164. feature "PSMD2" (0.000037)
2165. feature "GATD1" (0.000037)
2166. feature "HAUS7" (0.000037)
2167. feature "IRF2BP2" (0.000037)
2168. feature "MACROH2A2" (0.000037)
2169. feature "LPAR4" (0.000036)
2170. feature "FOXK2" (0.000036)
2171. feature "PIM3" (0.000036)
2172. feature "TTC39A" (0.000036)
2173. feature "DAPK3" (0.000036)
2174. feature "RCC1" (0.000036)
2175. feature "ASF1B" (0.000036)
2176. feature "DPP9" (0.000036)
2177. feature "CCDC149" (0.000036)
2178. feature "GAMT" (0.000036)
2179. feature "RAPGEFL1" (0.000036)
2180. feature "P4HA2" (0.000036)
2181. feature "VSIR" (0.000036)
2182. feature "FBXL18" (0.000036)
2183. feature "CC2D1A" (0.000036)
2184. feature "PABIR2" (0.000036)
2185. feature "ADGRV1" (0.000036)
2186. feature "TUBA5P" (0.000036)
2187. feature "PIK3CA" (0.000036)
2188. feature "MTARC1" (0.000036)
2189. feature "THAP1" (0.000036)
2190. feature "XRRA1" (0.000036)
2191. feature "RUNDC1" (0.000036)
2192. feature "TNFRSF10B" (0.000036)
2193. feature "TSHZ1" (0.000036)
2194. feature "MID1IP1" (0.000036)
2195. feature "ACYP2" (0.000036)
2196. feature "CTNNAL1" (0.000036)
2197. feature "MANBAL" (0.000036)
2198. feature "CST6" (0.000036)
2199. feature "PPP1R8" (0.000036)
2200. feature "AAMDC" (0.000036)
2201. feature "NCK2" (0.000036)
2202. feature "RNASET2" (0.000036)
2203. feature "ST14" (0.000036)
2204. feature "ZNF620" (0.000036)
2205. feature "DHRS4" (0.000036)
2206. feature "CEP162" (0.000035)
2207. feature "ELP1" (0.000035)
2208. feature "CPEB4" (0.000035)
2209. feature "KIF26A" (0.000035)
2210. feature "AK1" (0.000035)
2211. feature "PGAM5" (0.000035)
2212. feature "FPGS" (0.000035)
2213. feature "RTL8A" (0.000035)
2214. feature "IL18" (0.000035)
2215. feature "CCNG2" (0.000035)
2216. feature "GTF3C2" (0.000035)
2217. feature "SUCLG2" (0.000035)
2218. feature "PDPK1" (0.000035)
2219. feature "GRIN2D" (0.000035)
2220. feature "PLAA" (0.000035)
2221. feature "TIGAR" (0.000035)
2222. feature "MAN2B2" (0.000035)
2223. feature "RUNX2" (0.000035)
2224. feature "HCG11" (0.000035)
2225. feature "INSR" (0.000035)
2226. feature "CABLES2" (0.000035)
2227. feature "MED11" (0.000035)
2228. feature "KIF18B" (0.000035)
2229. feature "UNC119" (0.000035)
2230. feature "AMZ2P1" (0.000034)
2231. feature "NRSN2-AS1" (0.000034)
2232. feature "STYX" (0.000034)
2233. feature "ZMIZ2" (0.000034)
2234. feature "MCM3AP" (0.000034)
2235. feature "ABCA1" (0.000034)
2236. feature "MAP1A" (0.000034)
2237. feature "PRSS8" (0.000034)
2238. feature "GNG4" (0.000034)
2239. feature "TGM2" (0.000034)
2240. feature "RAD23A" (0.000034)
2241. feature "FYN" (0.000034)
2242. feature "SLC25A39" (0.000034)
2243. feature "SELENON" (0.000034)
2244. feature "SRGAP2" (0.000034)
2245. feature "ZSCAN18" (0.000034)
2246. feature "C12orf60" (0.000034)
2247. feature "ZZEF1" (0.000034)
2248. feature "GUCD1" (0.000034)
2249. feature "CACNA1H" (0.000034)
2250. feature "IRX4" (0.000034)
2251. feature "PGAP6" (0.000034)
2252. feature "C1QTNF6" (0.000034)
2253. feature "ANKRD28" (0.000034)
2254. feature "PHLDA1" (0.000034)
2255. feature "OR2C3" (0.000034)
2256. feature "NRAV" (0.000034)
2257. feature "MMP2" (0.000034)
2258. feature "UBXN6" (0.000034)
2259. feature "KLHL22" (0.000034)
2260. feature "AGPAT2" (0.000034)
2261. feature "SMTN" (0.000034)
2262. feature "TFIP11" (0.000033)
2263. feature "STK25" (0.000033)
2264. feature "NAXD" (0.000033)
2265. feature "TRAF3IP2" (0.000033)
2266. feature "RBSN" (0.000033)
2267. feature "ACSL4" (0.000033)
2268. feature "SEMA3D" (0.000033)
2269. feature "ACVR1" (0.000033)
2270. feature "BCAM" (0.000033)
2271. feature "RAB15" (0.000033)
2272. feature "PLD5" (0.000033)
2273. feature "DONSON" (0.000033)
2274. feature "PM20D2" (0.000033)
2275. feature "PTPN21" (0.000033)
2276. feature "PRXL2C" (0.000033)
2277. feature "PABPC1L" (0.000033)
2278. feature "TRIOBP" (0.000033)
2279. feature "PRICKLE2" (0.000033)
2280. feature "MTMR4" (0.000033)
2281. feature "CAPG" (0.000033)
2282. feature "PPIL1" (0.000033)
2283. feature "TNPO2" (0.000033)
2284. feature "RPS6KA4" (0.000033)
2285. feature "DISP1" (0.000033)
2286. feature "GNB2" (0.000033)
2287. feature "ZNF768" (0.000033)
2288. feature "GPM6B" (0.000033)
2289. feature "LIPE-AS1" (0.000033)
2290. feature "PRXL2B" (0.000033)
2291. feature "TAF1C" (0.000033)
2292. feature "PAM" (0.000033)
2293. feature "DYNLT1" (0.000033)
2294. feature "ARFGAP1" (0.000033)
2295. feature "DTD1" (0.000033)
2296. feature "SFTA1P" (0.000033)
2297. feature "PDLIM4" (0.000033)
2298. feature "CACTIN" (0.000033)
2299. feature "TRMT1L" (0.000033)
2300. feature "OR4F17" (0.000033)
2301. feature "PANX2" (0.000033)
2302. feature "C19orf54" (0.000033)
2303. feature "NUMBL" (0.000033)
2304. feature "RIPOR1" (0.000032)
2305. feature "OGFRL1" (0.000032)
2306. feature "RENO1" (0.000032)
2307. feature "CLEC11A" (0.000032)
2308. feature "PISD" (0.000032)
2309. feature "ACER3" (0.000032)
2310. feature "SCLY" (0.000032)
2311. feature "MAP3K3" (0.000032)
2312. feature "RAB3B" (0.000032)
2313. feature "POLR3E" (0.000032)
2314. feature "MSTO1" (0.000032)
2315. feature "MICAL2" (0.000032)
2316. feature "CPSF4" (0.000032)
2317. feature "PRDX1" (0.000032)
2318. feature "RAD54L" (0.000032)
2319. feature "DIABLO" (0.000032)
2320. feature "DNAJA4" (0.000032)
2321. feature "MZT1" (0.000032)
2322. feature "C15orf39" (0.000032)
2323. feature "DYNLT3" (0.000032)
2324. feature "JADE3" (0.000032)
2325. feature "ZNF317" (0.000032)
2326. feature "KIAA0895L" (0.000032)
2327. feature "ALDH2" (0.000032)
2328. feature "TRAK2" (0.000032)
2329. feature "PPP1R12B" (0.000032)
2330. feature "PSMF1" (0.000032)
2331. feature "CAMK2B" (0.000032)
2332. feature "SAMD8" (0.000032)
2333. feature "FBXO46" (0.000032)
2334. feature "MRTFA" (0.000032)
2335. feature "UBN2" (0.000032)
2336. feature "GPR3" (0.000032)
2337. feature "FBXO32" (0.000032)
2338. feature "CASKIN2" (0.000032)
2339. feature "TXN" (0.000032)
2340. feature "AAGAB" (0.000031)
2341. feature "JMJD8" (0.000031)
2342. feature "ESRRA" (0.000031)
2343. feature "MAD2L1" (0.000031)
2344. feature "ZNF764" (0.000031)
2345. feature "TNK2" (0.000031)
2346. feature "TRAF3" (0.000031)
2347. feature "EPN3" (0.000031)
2348. feature "FAM13A" (0.000031)
2349. feature "DBF4B" (0.000031)
2350. feature "DUSP10" (0.000031)
2351. feature "SRSF8" (0.000031)
2352. feature "MMP25-AS1" (0.000031)
2353. feature "COA5" (0.000031)
2354. feature "TRMT5" (0.000031)
2355. feature "SH2B1" (0.000031)
2356. feature "DCAF4" (0.000031)
2357. feature "ZNF335" (0.000031)
2358. feature "E4F1" (0.000031)
2359. feature "UNC13D" (0.000031)
2360. feature "RHOBTB2" (0.000031)
2361. feature "PLPPR2" (0.000031)
2362. feature "PPP2R3B" (0.000031)
2363. feature "ATP6V1H" (0.000031)
2364. feature "MAPKAPK5" (0.000031)
2365. feature "PLCD1" (0.000031)
2366. feature "ELP2" (0.000031)
2367. feature "CUEDC2" (0.000031)
2368. feature "AP1S2" (0.000031)
2369. feature "C16orf74" (0.000031)
2370. feature "DEAF1" (0.000031)
2371. feature "RNFT2" (0.000031)
2372. feature "TUFT1" (0.000031)
2373. feature "MAPK8IP2" (0.000031)
2374. feature "FAM20C" (0.000031)
2375. feature "ENTR1" (0.000031)
2376. feature "MAGED2" (0.000031)
2377. feature "NEBL" (0.000031)
2378. feature "MOCOS" (0.000031)
2379. feature "NETO2" (0.000031)
2380. feature "CIAO3" (0.000031)
2381. feature "TIGD2" (0.000030)
2382. feature "UBE2S" (0.000030)
2383. feature "THBS3" (0.000030)
2384. feature "BICDL1" (0.000030)
2385. feature "HECTD2" (0.000030)
2386. feature "TUBA1B" (0.000030)
2387. feature "OSTF1" (0.000030)
2388. feature "RALGPS1" (0.000030)
2389. feature "MPDZ" (0.000030)
2390. feature "SULT1B1" (0.000030)
2391. feature "PERP" (0.000030)
2392. feature "LINC01151" (0.000030)
2393. feature "MAML2" (0.000030)
2394. feature "MTATP6P1" (0.000030)
2395. feature "WARS2" (0.000030)
2396. feature "RAB8B" (0.000030)
2397. feature "NRP1" (0.000030)
2398. feature "MTFP1" (0.000030)
2399. feature "CRYL1" (0.000030)
2400. feature "FMN1" (0.000030)
2401. feature "TBC1D22A" (0.000030)
2402. feature "TAF8" (0.000030)
2403. feature "TP53I11" (0.000030)
2404. feature "HAND2-AS1" (0.000030)
2405. feature "NAPRT" (0.000030)
2406. feature "HAS2" (0.000030)
2407. feature "NTAN1" (0.000030)
2408. feature "RPGR" (0.000030)
2409. feature "FUT2" (0.000030)
2410. feature "EEF2K" (0.000030)
2411. feature "ABCB9" (0.000030)
2412. feature "SPDYE3" (0.000030)
2413. feature "ZNF33B" (0.000030)
2414. feature "KCTD1" (0.000030)
2415. feature "CDK7" (0.000030)
2416. feature "LRATD1" (0.000030)
2417. feature "FBXL20" (0.000030)
2418. feature "NSMAF" (0.000030)
2419. feature "PLK4" (0.000030)
2420. feature "DNA2" (0.000030)
2421. feature "TFPI2" (0.000030)
2422. feature "MT-TM" (0.000030)
2423. feature "PHF12" (0.000030)
2424. feature "POLA2" (0.000030)
2425. feature "PPRC1" (0.000030)
2426. feature "SETD1A" (0.000030)
2427. feature "ATF3" (0.000030)
2428. feature "ELOVL7" (0.000030)
2429. feature "ITGB8-AS1" (0.000029)
2430. feature "BEND7" (0.000029)
2431. feature "TMEM94" (0.000029)
2432. feature "SLC52A1" (0.000029)
2433. feature "ZNF696" (0.000029)
2434. feature "ALG13" (0.000029)
2435. feature "ARHGAP39" (0.000029)
2436. feature "MAFK" (0.000029)
2437. feature "CDADC1" (0.000029)
2438. feature "GCAT" (0.000029)
2439. feature "FMNL3" (0.000029)
2440. feature "GATA2-AS1" (0.000029)
2441. feature "ENTPD1" (0.000029)
2442. feature "FBXO2" (0.000029)
2443. feature "TFE3" (0.000029)
2444. feature "GPT2" (0.000029)
2445. feature "BATF" (0.000029)
2446. feature "FBXO48" (0.000029)
2447. feature "SQOR" (0.000029)
2448. feature "SOX13" (0.000029)
2449. feature "HNF4A" (0.000029)
2450. feature "DENND1B" (0.000029)
2451. feature "TCEANC" (0.000029)
2452. feature "FKTN" (0.000029)
2453. feature "SERGEF" (0.000029)
2454. feature "LHPP" (0.000029)
2455. feature "AUNIP" (0.000029)
2456. feature "TBC1D4" (0.000029)
2457. feature "ENKD1" (0.000029)
2458. feature "MOV10" (0.000029)
2459. feature "IMPA2" (0.000029)
2460. feature "RCC1L" (0.000029)
2461. feature "DGUOK-AS1" (0.000029)
2462. feature "ANKZF1" (0.000029)
2463. feature "ZFP36" (0.000029)
2464. feature "KLF10" (0.000029)
2465. feature "IGHMBP2" (0.000029)
2466. feature "APBA2" (0.000029)
2467. feature "ERVK3-1" (0.000029)
2468. feature "MAP3K10" (0.000029)
2469. feature "WDFY2" (0.000029)
2470. feature "FASTKD5" (0.000028)
2471. feature "HIP1R" (0.000028)
2472. feature "CEP85" (0.000028)
2473. feature "CEP170B" (0.000028)
2474. feature "BEX5" (0.000028)
2475. feature "C14orf28" (0.000028)
2476. feature "ELMOD3" (0.000028)
2477. feature "CEBPB" (0.000028)
2478. feature "PLA2G15" (0.000028)
2479. feature "BACH2" (0.000028)
2480. feature "RNASEH1-AS1" (0.000028)
2481. feature "TRAPPC14" (0.000028)
2482. feature "SDF2L1" (0.000028)
2483. feature "ARVCF" (0.000028)
2484. feature "ATXN7L1" (0.000028)
2485. feature "RAB40C" (0.000028)
2486. feature "SNX8" (0.000028)
2487. feature "GSDME" (0.000028)
2488. feature "HEBP1" (0.000028)
2489. feature "RAP1GAP2" (0.000028)
2490. feature "KAT2B" (0.000028)
2491. feature "HELZ2" (0.000028)
2492. feature "TPRN" (0.000028)
2493. feature "ZBTB33" (0.000028)
2494. feature "TMX3" (0.000028)
2495. feature "C11orf49" (0.000028)
2496. feature "FAH" (0.000028)
2497. feature "ZNF773" (0.000028)
2498. feature "CAPN15" (0.000028)
2499. feature "ZNF395" (0.000028)
2500. feature "TGFB1" (0.000028)
2501. feature "UPP1" (0.000028)
2502. feature "IKBKG" (0.000028)
2503. feature "COA7" (0.000028)
2504. feature "ZNF578" (0.000028)
2505. feature "ACVR2B" (0.000028)
2506. feature "DTX3L" (0.000028)
2507. feature "CIC" (0.000028)
2508. feature "ZDHHC8" (0.000028)
2509. feature "E2F1" (0.000028)
2510. feature "SH3BP5L" (0.000028)
2511. feature "DEPDC1B" (0.000028)
2512. feature "TBC1D7" (0.000028)
2513. feature "VPS18" (0.000028)
2514. feature "AFG3L1P" (0.000028)
2515. feature "LRCH3" (0.000027)
2516. feature "FDXR" (0.000027)
2517. feature "OVCH1-AS1" (0.000027)
2518. feature "BMPR1A" (0.000027)
2519. feature "DCLRE1A" (0.000027)
2520. feature "FBXO7" (0.000027)
2521. feature "CCDC18" (0.000027)
2522. feature "MAN2B1" (0.000027)
2523. feature "ONECUT2" (0.000027)
2524. feature "ZMAT3" (0.000027)
2525. feature "POLB" (0.000027)
2526. feature "RBM24" (0.000027)
2527. feature "PTPN23" (0.000027)
2528. feature "SLC25A22" (0.000027)
2529. feature "IFFO2" (0.000027)
2530. feature "TWIST1" (0.000027)
2531. feature "LIPT1" (0.000027)
2532. feature "FHIP1B" (0.000027)
2533. feature "HELB" (0.000027)
2534. feature "ABCF2" (0.000027)
2535. feature "ANKEF1" (0.000027)
2536. feature "FOXN3-AS1" (0.000027)
2537. feature "FDX2" (0.000027)
2538. feature "PRKAG1" (0.000027)
2539. feature "ZIC2" (0.000027)
2540. feature "EYA3" (0.000027)
2541. feature "NAA16" (0.000027)
2542. feature "BICC1" (0.000027)
2543. feature "NUDT8" (0.000027)
2544. feature "TBP" (0.000027)
2545. feature "FGFR3" (0.000027)
2546. feature "GTF2IRD1" (0.000027)
2547. feature "NAGLU" (0.000027)
2548. feature "NKX3-1" (0.000027)
2549. feature "CADM1" (0.000027)
2550. feature "FAAP100" (0.000027)
2551. feature "KHK" (0.000027)
2552. feature "JAKMIP3" (0.000027)
2553. feature "ESR1" (0.000027)
2554. feature "THAP4" (0.000027)
2555. feature "PYCR3" (0.000027)
2556. feature "GJB3" (0.000027)
2557. feature "LRRC69" (0.000027)
2558. feature "MED18" (0.000027)
2559. feature "NECTIN2" (0.000027)
2560. feature "TRAFD1" (0.000027)
2561. feature "ATXN7L2" (0.000027)
2562. feature "NFATC3" (0.000027)
2563. feature "DOCK9" (0.000027)
2564. feature "ZNF184" (0.000027)
2565. feature "SRC" (0.000027)
2566. feature "ZNF718" (0.000027)
2567. feature "PCED1B-AS1" (0.000027)
2568. feature "POLR3GL" (0.000027)
2569. feature "MTCO1P12" (0.000027)
2570. feature "NAA40" (0.000027)
2571. feature "RAD9A" (0.000027)
2572. feature "ATG4C" (0.000027)
2573. feature "CRAMP1" (0.000027)
2574. feature "TRDN" (0.000026)
2575. feature "TRIM69" (0.000026)
2576. feature "MIOS" (0.000026)
2577. feature "RNF19B" (0.000026)
2578. feature "CRYZL1" (0.000026)
2579. feature "SNX33" (0.000026)
2580. feature "TAF1A" (0.000026)
2581. feature "CALHM2" (0.000026)
2582. feature "IPO13" (0.000026)
2583. feature "IMPACT" (0.000026)
2584. feature "MED23" (0.000026)
2585. feature "ZNF180" (0.000026)
2586. feature "DYRK1B" (0.000026)
2587. feature "PLCL2" (0.000026)
2588. feature "KIF13B" (0.000026)
2589. feature "DCAKD" (0.000026)
2590. feature "YPEL5" (0.000026)
2591. feature "TRIM21" (0.000026)
2592. feature "SLC66A2" (0.000026)
2593. feature "NCOA5" (0.000026)
2594. feature "C2orf27A" (0.000026)
2595. feature "GEN1" (0.000026)
2596. feature "ZUP1" (0.000026)
2597. feature "ANKRD27" (0.000026)
2598. feature "LIN54" (0.000026)
2599. feature "SLC2A6" (0.000026)
2600. feature "EDC4" (0.000026)
2601. feature "HPS1" (0.000026)
2602. feature "PIH1D2" (0.000026)
2603. feature "PEF1" (0.000026)
2604. feature "CEP72" (0.000026)
2605. feature "PRELID2" (0.000026)
2606. feature "PLEKHG4B" (0.000026)
2607. feature "TBC1D16" (0.000026)
2608. feature "IQSEC2" (0.000026)
2609. feature "ZCCHC3" (0.000026)
2610. feature "EPHA2" (0.000026)
2611. feature "WDR90" (0.000026)
2612. feature "TGFB1I1" (0.000026)
2613. feature "PLEKHO1" (0.000026)
2614. feature "CXADR" (0.000026)
2615. feature "PPTC7" (0.000026)
2616. feature "FAM114A2" (0.000026)
2617. feature "MRPL49" (0.000026)
2618. feature "SULT2B1" (0.000026)
2619. feature "MIR200CHG" (0.000026)
2620. feature "LCA5" (0.000026)
2621. feature "TENT2" (0.000026)
2622. feature "CEBPG" (0.000026)
2623. feature "LINC01232" (0.000026)
2624. feature "FAM47E" (0.000026)
2625. feature "NCDN" (0.000026)
2626. feature "LRAT" (0.000026)
2627. feature "PHF21A" (0.000026)
2628. feature "ADAP1" (0.000026)
2629. feature "TBC1D22B" (0.000026)
2630. feature "CCDC97" (0.000026)
2631. feature "CASTOR2" (0.000026)
2632. feature "LINC00491" (0.000026)
2633. feature "GPN1" (0.000025)
2634. feature "ZDHHC4" (0.000025)
2635. feature "ZNF654" (0.000025)
2636. feature "TADA2B" (0.000025)
2637. feature "ZNF865" (0.000025)
2638. feature "SNHG17" (0.000025)
2639. feature "LTBP3" (0.000025)
2640. feature "ZNF329" (0.000025)
2641. feature "IKZF4" (0.000025)
2642. feature "SAP30-DT" (0.000025)
2643. feature "WDFY3-AS2" (0.000025)
2644. feature "UBALD1" (0.000025)
2645. feature "SLC9A8" (0.000025)
2646. feature "PIP4P2" (0.000025)
2647. feature "PTAR1" (0.000025)
2648. feature "ULK2" (0.000025)
2649. feature "ATP1A1-AS1" (0.000025)
2650. feature "MIB2" (0.000025)
2651. feature "MYO9B" (0.000025)
2652. feature "FBXO27" (0.000025)
2653. feature "ADD2" (0.000025)
2654. feature "TJAP1" (0.000025)
2655. feature "KIRREL1" (0.000025)
2656. feature "ARHGEF39" (0.000025)
2657. feature "DLX1" (0.000025)
2658. feature "RAB17" (0.000025)
2659. feature "PIGC" (0.000025)
2660. feature "SEMA4C" (0.000025)
2661. feature "IFRD2" (0.000025)
2662. feature "ARHGAP26" (0.000025)
2663. feature "LINC01750" (0.000025)
2664. feature "NEDD9" (0.000025)
2665. feature "HEIH" (0.000025)
2666. feature "ITGB6" (0.000025)
2667. feature "SRXN1" (0.000025)
2668. feature "ITPKC" (0.000025)
2669. feature "CDKL1" (0.000025)
2670. feature "SLC43A2" (0.000025)
2671. feature "SPINDOC" (0.000025)
2672. feature "ALDH4A1" (0.000025)
2673. feature "NRGN" (0.000025)
2674. feature "RAB11FIP5" (0.000025)
2675. feature "NAIF1" (0.000025)
2676. feature "SLC39A14" (0.000025)
2677. feature "LINC02482" (0.000025)
2678. feature "NDEL1" (0.000025)
2679. feature "TSEN54" (0.000025)
2680. feature "BIN3" (0.000025)
2681. feature "MEX3D" (0.000025)
2682. feature "ASB6" (0.000025)
2683. feature "RRAGC" (0.000025)
2684. feature "UNK" (0.000025)
2685. feature "SAP30L" (0.000025)
2686. feature "DHCR7" (0.000025)
2687. feature "CUTC" (0.000025)
2688. feature "MSX2" (0.000024)
2689. feature "ZNF875" (0.000024)
2690. feature "KHDC1" (0.000024)
2691. feature "NEFL" (0.000024)
2692. feature "KBTBD4" (0.000024)
2693. feature "COL12A1" (0.000024)
2694. feature "IRGQ" (0.000024)
2695. feature "NOSTRIN" (0.000024)
2696. feature "FAM171B" (0.000024)
2697. feature "TMEM126A" (0.000024)
2698. feature "YEATS2" (0.000024)
2699. feature "KCTD18" (0.000024)
2700. feature "C1orf53" (0.000024)
2701. feature "DNAJB2" (0.000024)
2702. feature "CBWD6" (0.000024)
2703. feature "PARP9" (0.000024)
2704. feature "TRIM16L" (0.000024)
2705. feature "KRTAP5-AS1" (0.000024)
2706. feature "TROAP" (0.000024)
2707. feature "LAMA3" (0.000024)
2708. feature "ZNF446" (0.000024)
2709. feature "ZHX2" (0.000024)
2710. feature "DECR2" (0.000024)
2711. feature "ERVK13-1" (0.000024)
2712. feature "KIAA0825" (0.000024)
2713. feature "GRB14" (0.000024)
2714. feature "DLK2" (0.000024)
2715. feature "EEF1A2" (0.000024)
2716. feature "MRPS36" (0.000024)
2717. feature "MEGF6" (0.000024)
2718. feature "ZNF597" (0.000024)
2719. feature "LINC02484" (0.000024)
2720. feature "FNBP1P1" (0.000024)
2721. feature "ATP5PDP3" (0.000024)
2722. feature "MTO1" (0.000024)
2723. feature "IPMK" (0.000024)
2724. feature "CAMKK1" (0.000024)
2725. feature "SNHG19" (0.000023)
2726. feature "BCL2L15" (0.000023)
2727. feature "PRPF40B" (0.000023)
2728. feature "UBALD2" (0.000023)
2729. feature "EN2" (0.000023)
2730. feature "NCAPG" (0.000023)
2731. feature "EIF3J-DT" (0.000023)
2732. feature "ZFP30" (0.000023)
2733. feature "TNFAIP2" (0.000023)
2734. feature "FABP6" (0.000023)
2735. feature "PROM2" (0.000023)
2736. feature "CDK8" (0.000023)
2737. feature "SMARCAL1" (0.000023)
2738. feature "WARS1" (0.000023)
2739. feature "FOSB" (0.000023)
2740. feature "PGBD1" (0.000023)
2741. feature "PACC1" (0.000023)
2742. feature "CXCL8" (0.000023)
2743. feature "DUSP8" (0.000023)
2744. feature "ST8SIA6-AS1" (0.000023)
2745. feature "RCHY1" (0.000023)
2746. feature "TEPSIN" (0.000023)
2747. feature "NOLC1" (0.000023)
2748. feature "RAP2A" (0.000023)
2749. feature "BAHD1" (0.000023)
2750. feature "NXNL2" (0.000023)
2751. feature "PROSER3" (0.000023)
2752. feature "CCM2" (0.000023)
2753. feature "TRIM66" (0.000023)
2754. feature "LTBP4" (0.000023)
2755. feature "HERC6" (0.000023)
2756. feature "HLA-C" (0.000023)
2757. feature "ZNF442" (0.000023)
2758. feature "IL21R" (0.000023)
2759. feature "PTK2B" (0.000023)
2760. feature "NFKBIZ" (0.000023)
2761. feature "CCDC71L" (0.000023)
2762. feature "SLC29A2" (0.000023)
2763. feature "MFAP2" (0.000023)
2764. feature "CALML5" (0.000023)
2765. feature "REPS2" (0.000023)
2766. feature "SLC25A43" (0.000023)
2767. feature "MLH3" (0.000023)
2768. feature "NDST1" (0.000023)
2769. feature "HES6" (0.000023)
2770. feature "ZFYVE27" (0.000023)
2771. feature "GSK3A" (0.000023)
2772. feature "CCDC88B" (0.000023)
2773. feature "PRKCH" (0.000023)
2774. feature "MTLN" (0.000023)
2775. feature "ARHGEF37" (0.000023)
2776. feature "SPATA6L" (0.000023)
2777. feature "PCLO" (0.000023)
2778. feature "RASSF8" (0.000023)
2779. feature "MAP3K1" (0.000023)
2780. feature "DFFB" (0.000023)
2781. feature "PRR36" (0.000023)
2782. feature "MBTPS1" (0.000022)
2783. feature "EPCAM-DT" (0.000022)
2784. feature "RIC1" (0.000022)
2785. feature "PDIA5" (0.000022)
2786. feature "PLCG1" (0.000022)
2787. feature "ARL17B" (0.000022)
2788. feature "BCKDHB" (0.000022)
2789. feature "WDR74" (0.000022)
2790. feature "CLASRP" (0.000022)
2791. feature "SMIM19" (0.000022)
2792. feature "PTPRU" (0.000022)
2793. feature "KIFC2" (0.000022)
2794. feature "ANKMY1" (0.000022)
2795. feature "VPS16" (0.000022)
2796. feature "CARD16" (0.000022)
2797. feature "UNC119B" (0.000022)
2798. feature "HIVEP3" (0.000022)
2799. feature "EGLN2" (0.000022)
2800. feature "ZNF35" (0.000022)
2801. feature "CDHR3" (0.000022)
2802. feature "TPTEP2" (0.000022)
2803. feature "ANKRD54" (0.000022)
2804. feature "DMD" (0.000022)
2805. feature "INSIG1" (0.000022)
2806. feature "ZIC4" (0.000022)
2807. feature "LINC00052" (0.000022)
2808. feature "MIATNB" (0.000022)
2809. feature "ZNF524" (0.000022)
2810. feature "SLC25A53" (0.000022)
2811. feature "PIKFYVE" (0.000022)
2812. feature "TRMT12" (0.000022)
2813. feature "WDR5B-DT" (0.000022)
2814. feature "YJU2" (0.000022)
2815. feature "ZKSCAN5" (0.000022)
2816. feature "TRAPPC9" (0.000022)
2817. feature "INPP5E" (0.000022)
2818. feature "TSPAN15" (0.000022)
2819. feature "RNF157" (0.000022)
2820. feature "ZNF813" (0.000022)
2821. feature "PANX1" (0.000022)
2822. feature "TNFRSF19" (0.000022)
2823. feature "RPL22L1" (0.000022)
2824. feature "SYT12" (0.000022)
2825. feature "PPIF" (0.000022)
2826. feature "WDR24" (0.000022)
2827. feature "OVOL1" (0.000022)
2828. feature "RNF183" (0.000022)
2829. feature "PTDSS2" (0.000022)
2830. feature "SMIM29" (0.000022)
2831. feature "EAF2" (0.000022)
2832. feature "LINC00326" (0.000022)
2833. feature "HOXC13-AS" (0.000022)
2834. feature "NHS" (0.000022)
2835. feature "HAP1" (0.000022)
2836. feature "SLC29A3" (0.000022)
2837. feature "SCNN1A" (0.000022)
2838. feature "LDB1" (0.000022)
2839. feature "PLCB1" (0.000021)
2840. feature "LINC01128" (0.000021)
2841. feature "MMP15" (0.000021)
2842. feature "ZNF254" (0.000021)
2843. feature "CDK5" (0.000021)
2844. feature "USP24" (0.000021)
2845. feature "NEU1" (0.000021)
2846. feature "CCN1" (0.000021)
2847. feature "TRAIP" (0.000021)
2848. feature "ROM1" (0.000021)
2849. feature "TRIM14" (0.000021)
2850. feature "THAP3" (0.000021)
2851. feature "DOLPP1" (0.000021)
2852. feature "TRAM2" (0.000021)
2853. feature "PHEX" (0.000021)
2854. feature "NT5C" (0.000021)
2855. feature "CERCAM" (0.000021)
2856. feature "JPH3" (0.000021)
2857. feature "KIAA0040" (0.000021)
2858. feature "UBTD1" (0.000021)
2859. feature "OXSM" (0.000021)
2860. feature "RNF146" (0.000021)
2861. feature "ITGB5" (0.000021)
2862. feature "PLIN3" (0.000021)
2863. feature "ZCWPW1" (0.000021)
2864. feature "MXD3" (0.000021)
2865. feature "PKP1" (0.000021)
2866. feature "BET1" (0.000021)
2867. feature "PIK3R2" (0.000021)
2868. feature "IGSF6" (0.000021)
2869. feature "POLM" (0.000021)
2870. feature "FARS2" (0.000021)
2871. feature "MFSD5" (0.000021)
2872. feature "ALDH6A1" (0.000021)
2873. feature "ZBTB12" (0.000021)
2874. feature "FAM219B" (0.000021)
2875. feature "MFSD2B" (0.000021)
2876. feature "UBIAD1" (0.000021)
2877. feature "SLC13A5" (0.000021)
2878. feature "HYAL2" (0.000021)
2879. feature "PAQR3" (0.000021)
2880. feature "FSBP" (0.000021)
2881. feature "GSTM3" (0.000021)
2882. feature "MAPK12" (0.000021)
2883. feature "TSC22D2" (0.000021)
2884. feature "LINC-PINT" (0.000021)
2885. feature "ARMH3" (0.000021)
2886. feature "IBA57" (0.000021)
2887. feature "LINC00885" (0.000021)
2888. feature "TSPAN17" (0.000021)
2889. feature "RRAGA" (0.000021)
2890. feature "NTN4" (0.000021)
2891. feature "BET1L" (0.000021)
2892. feature "ELF3" (0.000021)
2893. feature "SAT2" (0.000021)
2894. feature "XRCC1" (0.000021)
2895. feature "MID2" (0.000021)
2896. feature "SH3D19" (0.000021)
2897. feature "UST" (0.000021)
2898. feature "FAM193A" (0.000021)
2899. feature "GPATCH3" (0.000021)
2900. feature "PI4K2B" (0.000021)
2901. feature "RSPH3" (0.000021)
2902. feature "TMPRSS4" (0.000021)
2903. feature "ZNF99" (0.000021)
2904. feature "ZNF862" (0.000021)
2905. feature "ITM2C" (0.000021)
2906. feature "AGAP2-AS1" (0.000021)
2907. feature "IPPK" (0.000021)
2908. feature "RASSF7" (0.000021)
2909. feature "OAZ3" (0.000021)
2910. feature "BOK" (0.000021)
2911. feature "TMEM200A" (0.000021)
2912. feature "EFNA1" (0.000021)
2913. feature "CCDC18-AS1" (0.000021)
2914. feature "SPHK1" (0.000021)
2915. feature "ZFP41" (0.000021)
2916. feature "RAI1" (0.000021)
2917. feature "SLC40A1" (0.000020)
2918. feature "ERG28" (0.000020)
2919. feature "NANP" (0.000020)
2920. feature "RIPOR2" (0.000020)
2921. feature "TPBG" (0.000020)
2922. feature "SLC25A12" (0.000020)
2923. feature "HECA" (0.000020)
2924. feature "SUPT3H" (0.000020)
2925. feature "TGFB2" (0.000020)
2926. feature "SRCAP" (0.000020)
2927. feature "NARS2" (0.000020)
2928. feature "GORASP1" (0.000020)
2929. feature "TKFC" (0.000020)
2930. feature "THNSL2" (0.000020)
2931. feature "TXNRD3" (0.000020)
2932. feature "SLC7A11" (0.000020)
2933. feature "TPCN1" (0.000020)
2934. feature "RBP1" (0.000020)
2935. feature "FAM222A" (0.000020)
2936. feature "LINC00683" (0.000020)
2937. feature "FGFBP3" (0.000020)
2938. feature "ABCD4" (0.000020)
2939. feature "MAPK3" (0.000020)
2940. feature "RASGEF1A" (0.000020)
2941. feature "HES2" (0.000020)
2942. feature "SHROOM2" (0.000020)
2943. feature "NPFFR1" (0.000020)
2944. feature "LPCAT4" (0.000020)
2945. feature "FOXP4-AS1" (0.000020)
2946. feature "CROCCP3" (0.000020)
2947. feature "TTC7A" (0.000020)
2948. feature "ST6GAL1" (0.000020)
2949. feature "C10orf88" (0.000020)
2950. feature "IMPA1" (0.000020)
2951. feature "LINC00475" (0.000020)
2952. feature "GNPDA2" (0.000020)
2953. feature "LYPD6" (0.000020)
2954. feature "KIF20A" (0.000020)
2955. feature "ABCG1" (0.000020)
2956. feature "ZNF804A" (0.000020)
2957. feature "MORN1" (0.000020)
2958. feature "PC" (0.000020)
2959. feature "MUC5AC" (0.000020)
2960. feature "PSCA" (0.000020)
2961. feature "ZNF571-AS1" (0.000020)
2962. feature "KIFC3" (0.000020)
2963. feature "RALGDS" (0.000020)
2964. feature "MID1" (0.000020)
2965. feature "DYSF" (0.000020)
2966. feature "S100A4" (0.000020)
2967. feature "IL6R" (0.000020)
2968. feature "OCRL" (0.000020)
2969. feature "LINC01116" (0.000020)
2970. feature "OR9H1P" (0.000020)
2971. feature "TEX9" (0.000020)
2972. feature "ADCK1" (0.000020)
2973. feature "ECHDC2" (0.000020)
2974. feature "FOXM1" (0.000020)
2975. feature "SS18" (0.000020)
2976. feature "DDX28" (0.000020)
2977. feature "RTN4RL1" (0.000019)
2978. feature "CASP3" (0.000019)
2979. feature "DLX6" (0.000019)
2980. feature "OLFM2" (0.000019)
2981. feature "SLC35A2" (0.000019)
2982. feature "VMAC" (0.000019)
2983. feature "NLGN2" (0.000019)
2984. feature "ZDHHC18" (0.000019)
2985. feature "FAM53C" (0.000019)
2986. feature "SPIRE2" (0.000019)
2987. feature "NOVA1" (0.000019)
2988. feature "LRRC20" (0.000019)
2989. feature "ULK3" (0.000019)
2990. feature "CHD2" (0.000019)
2991. feature "MCEE" (0.000019)
2992. feature "HNRNPCP3" (0.000019)
2993. feature "ANKH" (0.000019)
2994. feature "RPIA" (0.000019)
2995. feature "MSX1" (0.000019)
2996. feature "ZNF134" (0.000019)
2997. feature "UQCRHL" (0.000019)
2998. feature "LY6D" (0.000019)
2999. feature "S1PR1" (0.000019)
3000. feature "ARHGEF17" (0.000019)
3001. feature "TRPM2" (0.000019)
3002. feature "ANXA6" (0.000019)
3003. feature "CEACAM19" (0.000019)
3004. feature "C5AR1" (0.000019)
3005. feature "SGSM2" (0.000019)
3006. feature "KIF3C" (0.000019)
3007. feature "HAAO" (0.000019)
3008. feature "PALM3" (0.000019)
3009. feature "NES" (0.000019)
3010. feature "INTS14" (0.000019)
3011. feature "TCF7" (0.000019)
3012. feature "UNC50" (0.000019)
3013. feature "CYHR1" (0.000019)
3014. feature "ARMC2" (0.000019)
3015. feature "C6orf136" (0.000019)
3016. feature "NINL" (0.000019)
3017. feature "POLR2J3" (0.000019)
3018. feature "FRMD8" (0.000019)
3019. feature "ATXN7L3" (0.000019)
3020. feature "ALS2CL" (0.000019)
3021. feature "B4GALT2" (0.000019)
3022. feature "AHR" (0.000019)
3023. feature "DNAH7" (0.000019)
3024. feature "MOGAT3" (0.000019)
3025. feature "MOGS" (0.000019)
3026. feature "TUBE1" (0.000019)
3027. feature "FANCF" (0.000019)
3028. feature "CIART" (0.000019)
3029. feature "PSG2" (0.000019)
3030. feature "HIGD1A" (0.000019)
3031. feature "SKIDA1" (0.000019)
3032. feature "CCDC7" (0.000019)
3033. feature "DOP1A" (0.000019)
3034. feature "CXCL1" (0.000019)
3035. feature "CYP1B1" (0.000019)
3036. feature "CAMTA2" (0.000019)
3037. feature "ILDR1" (0.000019)
3038. feature "TXNRD1" (0.000019)
3039. feature "FAM241B" (0.000019)
3040. feature "AP1B1" (0.000019)
3041. feature "DDAH1" (0.000019)
3042. feature "ARNTL" (0.000019)
3043. feature "ARL6IP1" (0.000019)
3044. feature "TMEM125" (0.000019)
3045. feature "FRAT1" (0.000019)
3046. feature "C1orf112" (0.000019)
3047. feature "MIS12" (0.000019)
3048. feature "TLE2" (0.000019)
3049. feature "ST3GAL1-DT" (0.000019)
3050. feature "RBMS3-AS3" (0.000019)
3051. feature "NSMCE3" (0.000019)
3052. feature "PXDN" (0.000018)
3053. feature "ATG101" (0.000018)
3054. feature "BOLA3-AS1" (0.000018)
3055. feature "OBSCN" (0.000018)
3056. feature "PHLPP1" (0.000018)
3057. feature "BDH1" (0.000018)
3058. feature "SNHG11" (0.000018)
3059. feature "FKBP9" (0.000018)
3060. feature "TSHZ2" (0.000018)
3061. feature "TIMM22" (0.000018)
3062. feature "ARMCX4" (0.000018)
3063. feature "TEAD3" (0.000018)
3064. feature "CRYZ" (0.000018)
3065. feature "DSC2" (0.000018)
3066. feature "HAS3" (0.000018)
3067. feature "ELFN2" (0.000018)
3068. feature "CDR2" (0.000018)
3069. feature "HR" (0.000018)
3070. feature "AMH" (0.000018)
3071. feature "PLA2G4A" (0.000018)
3072. feature "SLC2A8" (0.000018)
3073. feature "PXYLP1" (0.000018)
3074. feature "ACAA2" (0.000018)
3075. feature "CYB561D2" (0.000018)
3076. feature "OR7E13P" (0.000018)
3077. feature "IFI27" (0.000018)
3078. feature "SH3BGRL2" (0.000018)
3079. feature "LINC00173" (0.000018)
3080. feature "DVL1" (0.000018)
3081. feature "TRIM52-AS1" (0.000018)
3082. feature "UBE2QL1" (0.000018)
3083. feature "ZNF581" (0.000018)
3084. feature "RABL2A" (0.000018)
3085. feature "BICRA" (0.000018)
3086. feature "LINC02241" (0.000018)
3087. feature "ZNF731P" (0.000018)
3088. feature "UBE2D4" (0.000018)
3089. feature "OR3A3" (0.000018)
3090. feature "DOC2A" (0.000018)
3091. feature "OR5M2P" (0.000018)
3092. feature "RPUSD4" (0.000018)
3093. feature "TMEM44-AS1" (0.000018)
3094. feature "RAB20" (0.000018)
3095. feature "PGBD2" (0.000018)
3096. feature "PPP1R9B" (0.000018)
3097. feature "FAM169A" (0.000018)
3098. feature "PEX11B" (0.000018)
3099. feature "ZNF469" (0.000018)
3100. feature "DPF1" (0.000018)
3101. feature "PCAT14" (0.000018)
3102. feature "UMAD1" (0.000018)
3103. feature "HMGB1P39" (0.000018)
3104. feature "ARRDC3" (0.000018)
3105. feature "MAPK8IP3" (0.000018)
3106. feature "NBEAL2" (0.000018)
3107. feature "EREG" (0.000018)
3108. feature "TRERF1" (0.000018)
3109. feature "THRA" (0.000018)
3110. feature "PBXIP1" (0.000018)
3111. feature "SVIL-AS1" (0.000018)
3112. feature "PCAT1" (0.000018)
3113. feature "MHENCR" (0.000018)
3114. feature "TEDC2" (0.000018)
3115. feature "CLCF1" (0.000018)
3116. feature "GASAL1" (0.000018)
3117. feature "LINC02913" (0.000018)
3118. feature "GLI4" (0.000018)
3119. feature "MAU2" (0.000018)
3120. feature "TMEM241" (0.000018)
3121. feature "LRFN4" (0.000018)
3122. feature "NOTCH2NLC" (0.000018)
3123. feature "MVK" (0.000018)
3124. feature "LINC02609" (0.000018)
3125. feature "GAS7" (0.000017)
3126. feature "SUN2" (0.000017)
3127. feature "HBEGF" (0.000017)
3128. feature "GMEB1" (0.000017)
3129. feature "SCMH1" (0.000017)
3130. feature "C3orf14" (0.000017)
3131. feature "ZNF433" (0.000017)
3132. feature "APOBEC3F" (0.000017)
3133. feature "C9orf64" (0.000017)
3134. feature "FBXL16" (0.000017)
3135. feature "RNF25" (0.000017)
3136. feature "SAP30L-AS1" (0.000017)
3137. feature "MAPT-AS1" (0.000017)
3138. feature "NFKBIL1" (0.000017)
3139. feature "MFSD13A" (0.000017)
3140. feature "CES2" (0.000017)
3141. feature "LINC00639" (0.000017)
3142. feature "TDRD3" (0.000017)
3143. feature "BRWD1-AS2" (0.000017)
3144. feature "COL27A1" (0.000017)
3145. feature "GKAP1" (0.000017)
3146. feature "ALPK1" (0.000017)
3147. feature "KDM7A-DT" (0.000017)
3148. feature "NUSAP1" (0.000017)
3149. feature "STK35" (0.000017)
3150. feature "IFNGR2" (0.000017)
3151. feature "MICALL1" (0.000017)
3152. feature "TMEM51" (0.000017)
3153. feature "SLC36A4" (0.000017)
3154. feature "RPA1" (0.000017)
3155. feature "FAM107A" (0.000017)
3156. feature "HLCS" (0.000017)
3157. feature "ZBTB42" (0.000017)
3158. feature "TRAPPC2B" (0.000017)
3159. feature "DUOX1" (0.000017)
3160. feature "LSS" (0.000017)
3161. feature "PDE6D" (0.000017)
3162. feature "LACTB2-AS1" (0.000017)
3163. feature "SYNM" (0.000017)
3164. feature "SYVN1" (0.000017)
3165. feature "CYP4B1" (0.000017)
3166. feature "CTNNA1-AS1" (0.000017)
3167. feature "SMPD2" (0.000017)
3168. feature "MYOSLID" (0.000017)
3169. feature "NOC2LP1" (0.000017)
3170. feature "CCNYL1" (0.000017)
3171. feature "SPN" (0.000017)
3172. feature "BICRAL" (0.000017)
3173. feature "PTRHD1" (0.000017)
3174. feature "OR10H1" (0.000017)
3175. feature "SERTAD2" (0.000017)
3176. feature "PPP1R12A-AS1" (0.000017)
3177. feature "TBKBP1" (0.000017)
3178. feature "EPB41L4B" (0.000017)
3179. feature "ATP6V0D1-DT" (0.000017)
3180. feature "H2AZ1" (0.000017)
3181. feature "IGF2BP2" (0.000017)
3182. feature "PYGO1" (0.000017)
3183. feature "TANGO2" (0.000017)
3184. feature "CDC14A" (0.000017)
3185. feature "PARP12" (0.000017)
3186. feature "TBL3" (0.000017)
3187. feature "GOLGA2P7" (0.000017)
3188. feature "SNAPC3" (0.000017)
3189. feature "IFIT1" (0.000017)
3190. feature "AQP11" (0.000017)
3191. feature "SWSAP1" (0.000017)
3192. feature "PLXNA1" (0.000017)
3193. feature "CCN2" (0.000017)
3194. feature "NOA1" (0.000017)
3195. feature "TNFAIP3" (0.000017)
3196. feature "PALM" (0.000017)
3197. feature "PODNL1" (0.000017)
3198. feature "MTF1" (0.000017)
3199. feature "SRP14-DT" (0.000017)
3200. feature "RGP1" (0.000017)
3201. feature "ZNF321P" (0.000017)
3202. feature "VDR" (0.000017)
3203. feature "TRAF4" (0.000017)
3204. feature "TSGA10" (0.000017)
3205. feature "FAM222B" (0.000017)
3206. feature "IL4R" (0.000017)
3207. feature "CCNE2" (0.000017)
3208. feature "AMPD2" (0.000017)
3209. feature "RNF44" (0.000017)
3210. feature "PIP5K1C" (0.000016)
3211. feature "NOP2" (0.000016)
3212. feature "PCSK9" (0.000016)
3213. feature "DCLK1" (0.000016)
3214. feature "TRIM39" (0.000016)
3215. feature "PER1" (0.000016)
3216. feature "NDST2" (0.000016)
3217. feature "C21orf62-AS1" (0.000016)
3218. feature "PLEKHS1" (0.000016)
3219. feature "PRR5L" (0.000016)
3220. feature "PWWP2B" (0.000016)
3221. feature "CORO2A" (0.000016)
3222. feature "CCDC102A" (0.000016)
3223. feature "GPRIN1" (0.000016)
3224. feature "SELENOP" (0.000016)
3225. feature "UVSSA" (0.000016)
3226. feature "PKN3" (0.000016)
3227. feature "KIF27" (0.000016)
3228. feature "ALAD" (0.000016)
3229. feature "C2CD5" (0.000016)
3230. feature "PSMD9" (0.000016)
3231. feature "CHRNB1" (0.000016)
3232. feature "DHRS11" (0.000016)
3233. feature "KIF18A" (0.000016)
3234. feature "SULF2" (0.000016)
3235. feature "CDKN2B" (0.000016)
3236. feature "CDH6" (0.000016)
3237. feature "VCAN" (0.000016)
3238. feature "TRNP1" (0.000016)
3239. feature "KCP" (0.000016)
3240. feature "LNCARSR" (0.000016)
3241. feature "C5" (0.000016)
3242. feature "SFXN3" (0.000016)
3243. feature "RASD2" (0.000016)
3244. feature "RAB23" (0.000016)
3245. feature "PAX5" (0.000016)
3246. feature "PLD2" (0.000016)
3247. feature "LZTR1" (0.000016)
3248. feature "SH3PXD2A" (0.000016)
3249. feature "KDM5C" (0.000016)
3250. feature "STK40" (0.000016)
3251. feature "POMK" (0.000016)
3252. feature "PODXL2" (0.000016)
3253. feature "BCL2L2" (0.000016)
3254. feature "STRIP1" (0.000016)
3255. feature "SPHK2" (0.000016)
3256. feature "SYNGAP1" (0.000016)
3257. feature "SUNO1" (0.000016)
3258. feature "CCNG1" (0.000016)
3259. feature "GPR160" (0.000016)
3260. feature "STRIP2" (0.000016)
3261. feature "ALDH3B1" (0.000016)
3262. feature "SPSB1" (0.000016)
3263. feature "KBTBD3" (0.000016)
3264. feature "PLEKHG1" (0.000016)
3265. feature "G6PC3" (0.000016)
3266. feature "CARD10" (0.000016)
3267. feature "ZKSCAN8" (0.000016)
3268. feature "ZBTB39" (0.000016)
3269. feature "DSG2" (0.000016)
3270. feature "HMGA2" (0.000016)
3271. feature "BCL7B" (0.000016)
3272. feature "LCA5L" (0.000016)
3273. feature "GNAZ" (0.000016)
3274. feature "UBQLN2" (0.000016)
3275. feature "PRR15" (0.000016)
3276. feature "KDF1" (0.000016)
3277. feature "VAMP2" (0.000016)
3278. feature "TTC3P1" (0.000016)
3279. feature "MAZ" (0.000016)
3280. feature "SALRNA1" (0.000016)
3281. feature "CAB39L" (0.000016)
3282. feature "BMP8A" (0.000016)
3283. feature "MMP16" (0.000016)
3284. feature "PPP1R18" (0.000016)
3285. feature "STAT5B" (0.000016)
3286. feature "PEX7" (0.000016)
3287. feature "TMEM25" (0.000016)
3288. feature "FBXL3" (0.000016)
3289. feature "LINC01488" (0.000016)
3290. feature "DUSP1" (0.000016)
3291. feature "PARP6" (0.000016)
3292. feature "NOMO3" (0.000016)
3293. feature "ZXDC" (0.000016)
3294. feature "CREB3L1" (0.000016)
3295. feature "NECAP1" (0.000016)
3296. feature "CROCC" (0.000016)
3297. feature "CARD9" (0.000016)
3298. feature "LINC00471" (0.000016)
3299. feature "RAB36" (0.000016)
3300. feature "ZNF473" (0.000016)
3301. feature "TSACC" (0.000016)
3302. feature "ZNF767P" (0.000016)
3303. feature "TESMIN" (0.000016)
3304. feature "RBM15B" (0.000016)
3305. feature "CMTM4" (0.000016)
3306. feature "RADIL" (0.000016)
3307. feature "NUDT17" (0.000016)
3308. feature "ATP6V0C" (0.000016)
3309. feature "ZWINT" (0.000016)
3310. feature "RASGRP1" (0.000016)
3311. feature "CASP1" (0.000016)
3312. feature "MAPK7" (0.000016)
3313. feature "PACSIN1" (0.000015)
3314. feature "FOXJ3" (0.000015)
3315. feature "LIN52" (0.000015)
3316. feature "RILP" (0.000015)
3317. feature "SCOC-AS1" (0.000015)
3318. feature "ZNF699" (0.000015)
3319. feature "SIX2" (0.000015)
3320. feature "AKR1E2" (0.000015)
3321. feature "PER3" (0.000015)
3322. feature "RTL8C" (0.000015)
3323. feature "LINC01206" (0.000015)
3324. feature "SHISA2" (0.000015)
3325. feature "TEF" (0.000015)
3326. feature "LRRC37A3" (0.000015)
3327. feature "SLC35B3" (0.000015)
3328. feature "PARP16" (0.000015)
3329. feature "PML" (0.000015)
3330. feature "NAB2" (0.000015)
3331. feature "ZNF674" (0.000015)
3332. feature "HCN2" (0.000015)
3333. feature "LINC01963" (0.000015)
3334. feature "FLNC" (0.000015)
3335. feature "ALOX15" (0.000015)
3336. feature "C1QL1" (0.000015)
3337. feature "KCNMA1" (0.000015)
3338. feature "FCHO1" (0.000015)
3339. feature "OSBPL6" (0.000015)
3340. feature "CHMP6" (0.000015)
3341. feature "PHF19" (0.000015)
3342. feature "LINC00863" (0.000015)
3343. feature "ANKRD20A5P" (0.000015)
3344. feature "NOG" (0.000015)
3345. feature "DUSP9" (0.000015)
3346. feature "MLYCD" (0.000015)
3347. feature "STARD3" (0.000015)
3348. feature "DOCK8-AS1" (0.000015)
3349. feature "CRYBG2" (0.000015)
3350. feature "E2F2" (0.000015)
3351. feature "C12orf76" (0.000015)
3352. feature "BMS1P23" (0.000015)
3353. feature "SLC22A23" (0.000015)
3354. feature "RAB39B" (0.000015)
3355. feature "SMIM5" (0.000015)
3356. feature "UPRT" (0.000015)
3357. feature "PIK3IP1" (0.000015)
3358. feature "BMI1" (0.000015)
3359. feature "CRYBB2" (0.000015)
3360. feature "GLB1" (0.000015)
3361. feature "FBXO43" (0.000015)
3362. feature "KCNG1" (0.000015)
3363. feature "DNAJC6" (0.000015)
3364. feature "ABCA2" (0.000015)
3365. feature "PADI2" (0.000015)
3366. feature "EPHX2" (0.000015)
3367. feature "FIZ1" (0.000015)
3368. feature "DPY19L1P1" (0.000015)
3369. feature "OTULINL" (0.000015)
3370. feature "SYNPO2L" (0.000015)
3371. feature "HSP90AA2P" (0.000015)
3372. feature "N6AMT1" (0.000015)
3373. feature "AKR1B10" (0.000015)
3374. feature "IVL" (0.000015)
3375. feature "ABHD14B" (0.000015)
3376. feature "VASN" (0.000015)
3377. feature "RHOB" (0.000015)
3378. feature "SLC45A4" (0.000015)
3379. feature "ABCC13" (0.000015)
3380. feature "GRK4" (0.000015)
3381. feature "DSCR8" (0.000015)
3382. feature "PPP1R3B" (0.000015)
3383. feature "RTN4R" (0.000015)
3384. feature "GHDC" (0.000015)
3385. feature "FBXO38" (0.000015)
3386. feature "CYP27C1" (0.000015)
3387. feature "GRASLND" (0.000015)
3388. feature "TTLL1" (0.000015)
3389. feature "FRAS1" (0.000015)
3390. feature "HELQ" (0.000015)
3391. feature "DOK7" (0.000015)
3392. feature "MAP3K6" (0.000015)
3393. feature "CARF" (0.000015)
3394. feature "ASB13" (0.000015)
3395. feature "BHLHE40" (0.000015)
3396. feature "CSRNP1" (0.000015)
3397. feature "LAT" (0.000015)
3398. feature "KCTD17" (0.000015)
3399. feature "PCDHB9" (0.000015)
3400. feature "SBF1" (0.000015)
3401. feature "PBX3" (0.000015)
3402. feature "C8orf58" (0.000015)
3403. feature "FAM86EP" (0.000015)
3404. feature "AMZ1" (0.000014)
3405. feature "TMPRSS3" (0.000014)
3406. feature "CAPN5" (0.000014)
3407. feature "CCNB3P1" (0.000014)
3408. feature "MARS2" (0.000014)
3409. feature "CRTC2" (0.000014)
3410. feature "FOXD2" (0.000014)
3411. feature "ZCCHC14" (0.000014)
3412. feature "ITGB8" (0.000014)
3413. feature "FLVCR2" (0.000014)
3414. feature "ZFHX2" (0.000014)
3415. feature "ZNF514" (0.000014)
3416. feature "HLA-F" (0.000014)
3417. feature "BTBD7P1" (0.000014)
3418. feature "ZNF219" (0.000014)
3419. feature "LRRC8B" (0.000014)
3420. feature "SLC30A1" (0.000014)
3421. feature "CDK19" (0.000014)
3422. feature "RILPL2" (0.000014)
3423. feature "PCED1A" (0.000014)
3424. feature "ARPC1B" (0.000014)
3425. feature "CDX2" (0.000014)
3426. feature "PPP1R14C" (0.000014)
3427. feature "LRRC3" (0.000014)
3428. feature "SLC27A4" (0.000014)
3429. feature "LINC00680" (0.000014)
3430. feature "RPL12P13" (0.000014)
3431. feature "DNM3" (0.000014)
3432. feature "RFPL3S" (0.000014)
3433. feature "KCTD14" (0.000014)
3434. feature "MEMO1" (0.000014)
3435. feature "GOLT1A" (0.000014)
3436. feature "FHIP2B" (0.000014)
3437. feature "PLEKHF1" (0.000014)
3438. feature "SNX11" (0.000014)
3439. feature "SNAP25-AS1" (0.000014)
3440. feature "POC1A" (0.000014)
3441. feature "WDR6" (0.000014)
3442. feature "SLC2A13" (0.000014)
3443. feature "STAT2" (0.000014)
3444. feature "SEPTIN3" (0.000014)
3445. feature "PARD3B" (0.000014)
3446. feature "SHMT1" (0.000014)
3447. feature "RFNG" (0.000014)
3448. feature "RRP9" (0.000014)
3449. feature "TRIM11" (0.000014)
3450. feature "PCDHA10" (0.000014)
3451. feature "JADE1" (0.000014)
3452. feature "ZNF799" (0.000014)
3453. feature "PRELID3A" (0.000014)
3454. feature "COPG2IT1" (0.000014)
3455. feature "ID4" (0.000014)
3456. feature "ARHGEF2" (0.000014)
3457. feature "ZNF169" (0.000014)
3458. feature "HSF2BP" (0.000014)
3459. feature "TRAF6" (0.000014)
3460. feature "KLHL24" (0.000014)
3461. feature "TMEM178B" (0.000014)
3462. feature "GALNS" (0.000014)
3463. feature "TMEM52" (0.000014)
3464. feature "LINC02178" (0.000014)
3465. feature "FSTL1" (0.000014)
3466. feature "AAAS" (0.000014)
3467. feature "TBCCD1" (0.000014)
3468. feature "PLEKHG6" (0.000014)
3469. feature "STK16" (0.000014)
3470. feature "TTK" (0.000014)
3471. feature "ZNF879" (0.000014)
3472. feature "TSBP1-AS1" (0.000014)
3473. feature "CASP10" (0.000014)
3474. feature "ABHD14A" (0.000014)
3475. feature "LINC00345" (0.000014)
3476. feature "LINC01214" (0.000014)
3477. feature "H2AW" (0.000014)
3478. feature "LINC02428" (0.000014)
3479. feature "HMGN1P14" (0.000014)
3480. feature "EPB41L4A-AS1" (0.000014)
3481. feature "ASH1L-AS1" (0.000014)
3482. feature "TINF2" (0.000014)
3483. feature "TENT5C" (0.000014)
3484. feature "ILRUN-AS1" (0.000014)
3485. feature "OVOL2" (0.000014)
3486. feature "NRG4" (0.000014)
3487. feature "TCF15" (0.000014)
3488. feature "ZNF132-DT" (0.000014)
3489. feature "RNF215" (0.000014)
3490. feature "BCL2" (0.000014)
3491. feature "FES" (0.000014)
3492. feature "PROSER2" (0.000014)
3493. feature "FAM171A2" (0.000014)
3494. feature "IRF7" (0.000014)
3495. feature "TCL6" (0.000014)
3496. feature "DPPA4" (0.000014)
3497. feature "BORCS6" (0.000014)
3498. feature "SHF" (0.000014)
3499. feature "MAPK8" (0.000014)
3500. feature "PIGK" (0.000014)
3501. feature "ZNF550" (0.000014)
3502. feature "RBM38" (0.000014)
3503. feature "MAGED1" (0.000014)
3504. feature "ABCA7" (0.000014)
3505. feature "SLC66A1" (0.000014)
3506. feature "FAM120C" (0.000014)
3507. feature "CTHRC1" (0.000014)
3508. feature "GLMP" (0.000014)
3509. feature "ASPM" (0.000014)
3510. feature "WNT7A" (0.000014)
3511. feature "LINC02298" (0.000014)
3512. feature "LRP4" (0.000014)
3513. feature "NEU3" (0.000013)
3514. feature "ZNF263" (0.000013)
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3689. feature "ACOT2" (0.000012)
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3693. feature "PTPRB" (0.000012)
3694. feature "HOXA11-AS" (0.000012)
3695. feature "WASF3" (0.000012)
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3703. feature "ALDH5A1" (0.000012)
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3709. feature "HBP1" (0.000012)
3710. feature "PTPN6" (0.000012)
3711. feature "FSIP1" (0.000012)
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3718. feature "WDR53" (0.000011)
3719. feature "TMEM8B" (0.000011)
3720. feature "TXNDC15" (0.000011)
3721. feature "ZNF251" (0.000011)
3722. feature "TMTC2" (0.000011)
3723. feature "LINC02870" (0.000011)
3724. feature "MTFR1L" (0.000011)
3725. feature "RAET1E" (0.000011)
3726. feature "FAM110D" (0.000011)
3727. feature "CTH" (0.000011)
3728. feature "MIOS-DT" (0.000011)
3729. feature "DOP1B" (0.000011)
3730. feature "BCO2" (0.000011)
3731. feature "PANK1" (0.000011)
3732. feature "ORC1" (0.000011)
3733. feature "CTU2" (0.000011)
3734. feature "BMP4" (0.000011)
3735. feature "USP35" (0.000011)
3736. feature "MIR137HG" (0.000011)
3737. feature "RCCD1" (0.000011)
3738. feature "TP53INP2" (0.000011)
3739. feature "C7orf26" (0.000011)
3740. feature "SNORC" (0.000011)
3741. feature "DENND3" (0.000011)
3742. feature "SH3BP1" (0.000011)
3743. feature "CACHD1" (0.000011)
3744. feature "ACVR1C" (0.000011)
3745. feature "UGT1A7" (0.000011)
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3747. feature "VWA2" (0.000011)
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3750. feature "B9D2" (0.000011)
3751. feature "KLHL21" (0.000011)
3752. feature "RGMB" (0.000011)
3753. feature "GRIN1" (0.000011)
3754. feature "TRPC3" (0.000011)
3755. feature "SLC3A2" (0.000011)
3756. feature "SGMS1-AS1" (0.000011)
3757. feature "ALX1" (0.000011)
3758. feature "TGDS" (0.000011)
3759. feature "FURIN" (0.000011)
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3765. feature "GPR137C" (0.000011)
3766. feature "GSEC" (0.000011)
3767. feature "A4GALT" (0.000011)
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3769. feature "LINC00343" (0.000011)
3770. feature "ZNF250" (0.000011)
3771. feature "P2RX7" (0.000011)
3772. feature "KIF12" (0.000011)
3773. feature "DMPK" (0.000011)
3774. feature "KLHDC3" (0.000011)
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3776. feature "BRME1" (0.000011)
3777. feature "GREP1" (0.000011)
3778. feature "ANKRD44" (0.000011)
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3780. feature "MOB2" (0.000011)
3781. feature "NCAPG2" (0.000011)
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3783. feature "MCM3" (0.000011)
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3785. feature "CHST5" (0.000011)
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3787. feature "CRX" (0.000011)
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3790. feature "LTB4R" (0.000011)
3791. feature "CPNE2" (0.000011)
3792. feature "FAM47E-STBD1" (0.000011)
3793. feature "TMPRSS2" (0.000011)
3794. feature "ITGA4" (0.000011)
3795. feature "FAM131A" (0.000011)
3796. feature "UBE2L6" (0.000011)
3797. feature "PANK4" (0.000011)
3798. feature "SOX9" (0.000011)
3799. feature "TMEM104" (0.000011)
3800. feature "IL20RA" (0.000011)
3801. feature "MFAP3" (0.000011)
3802. feature "ZNF702P" (0.000011)
3803. feature "NTN1" (0.000011)
3804. feature "TFEB" (0.000011)
3805. feature "FAM185BP" (0.000011)
3806. feature "LINC01607" (0.000011)
3807. feature "EXOSC10-AS1" (0.000011)
3808. feature "ZC3H10" (0.000011)
3809. feature "BOLA1" (0.000010)
3810. feature "KYAT3" (0.000010)
3811. feature "DLX3" (0.000010)
3812. feature "PCDHGA1" (0.000010)
3813. feature "RNF26" (0.000010)
3814. feature "ZNF747" (0.000010)
3815. feature "FBLN2" (0.000010)
3816. feature "BEND3" (0.000010)
3817. feature "STAM-DT" (0.000010)
3818. feature "OPN3" (0.000010)
3819. feature "ZBED5-AS1" (0.000010)
3820. feature "DXO" (0.000010)
3821. feature "DELE1" (0.000010)
3822. feature "USP30" (0.000010)
3823. feature "ANKRD45" (0.000010)
3824. feature "CTDP1" (0.000010)
3825. feature "SNX18" (0.000010)
3826. feature "ZNF558" (0.000010)
3827. feature "ARHGEF26-AS1" (0.000010)
3828. feature "TBC1D25" (0.000010)
3829. feature "SYDE1" (0.000010)
3830. feature "RHOXF2B" (0.000010)
3831. feature "ANKRA2" (0.000010)
3832. feature "MPP3" (0.000010)
3833. feature "PRDM11" (0.000010)
3834. feature "STON1" (0.000010)
3835. feature "ZNF418" (0.000010)
3836. feature "PRRG2" (0.000010)
3837. feature "CDAN1" (0.000010)
3838. feature "ZNF77" (0.000010)
3839. feature "TEP1" (0.000010)
3840. feature "LINC01725" (0.000010)
3841. feature "DGLUCY" (0.000010)
3842. feature "KCNC3" (0.000010)
3843. feature "CD55" (0.000010)
3844. feature "SLC22A5" (0.000010)
3845. feature "FAM43A" (0.000010)
3846. feature "CTTN-DT" (0.000010)
3847. feature "PPOX" (0.000010)
3848. feature "DNAAF9" (0.000010)
3849. feature "PSPN" (0.000010)
3850. feature "CATSPERB" (0.000010)
3851. feature "TNFRSF11B" (0.000010)
3852. feature "TPST1" (0.000010)
3853. feature "DARS1-AS1" (0.000010)
3854. feature "ZKSCAN3" (0.000010)
3855. feature "ANXA9" (0.000010)
3856. feature "SETD9" (0.000010)
3857. feature "OR2A5" (0.000010)
3858. feature "PLEKHG5" (0.000010)
3859. feature "PYY2" (0.000010)
3860. feature "MELTF-AS1" (0.000010)
3861. feature "TRPV4" (0.000010)
3862. feature "NTF4" (0.000010)
3863. feature "PLD6" (0.000010)
3864. feature "IDI1" (0.000010)
3865. feature "B3GALT1" (0.000010)
3866. feature "MST1" (0.000010)
3867. feature "HLA-DPB1" (0.000010)
3868. feature "PPP2R2B" (0.000010)
3869. feature "KLC3" (0.000010)
3870. feature "MDM1" (0.000010)
3871. feature "SLC37A2" (0.000010)
3872. feature "WHAMMP3" (0.000010)
3873. feature "XYLB" (0.000010)
3874. feature "STK36" (0.000010)
3875. feature "DIXDC1" (0.000010)
3876. feature "RHD" (0.000010)
3877. feature "DMTN" (0.000010)
3878. feature "NLRX1" (0.000010)
3879. feature "ZNF22" (0.000010)
3880. feature "CASC9" (0.000010)
3881. feature "KCNE4" (0.000010)
3882. feature "LRRC23" (0.000010)
3883. feature "FBXL17" (0.000010)
3884. feature "TTC38" (0.000010)
3885. feature "KITLG" (0.000010)
3886. feature "IL18BP" (0.000010)
3887. feature "INPP4A" (0.000010)
3888. feature "CSF1" (0.000010)
3889. feature "ATG9A" (0.000010)
3890. feature "IFITM10" (0.000010)
3891. feature "CMYA5" (0.000010)
3892. feature "RMI2" (0.000010)
3893. feature "C11orf98" (0.000010)
3894. feature "ZNF461" (0.000010)
3895. feature "ZNF90" (0.000010)
3896. feature "ATP6V1B2" (0.000010)
3897. feature "RHBDF1" (0.000010)
3898. feature "RECQL5" (0.000010)
3899. feature "MPP2" (0.000010)
3900. feature "EFCAB1" (0.000010)
3901. feature "WDR19" (0.000010)
3902. feature "PROS1" (0.000010)
3903. feature "HERPUD2-AS1" (0.000010)
3904. feature "MYD88" (0.000010)
3905. feature "LENG8-AS1" (0.000010)
3906. feature "LINC02273" (0.000010)
3907. feature "FKBP4" (0.000010)
3908. feature "DNAJC3-DT" (0.000010)
3909. feature "PGAP3" (0.000010)
3910. feature "FZD1" (0.000010)
3911. feature "ERG" (0.000010)
3912. feature "VDAC1P8" (0.000010)
3913. feature "DPY19L2P1" (0.000010)
3914. feature "C1orf210" (0.000010)
3915. feature "BAMBI" (0.000010)
3916. feature "MT-TT" (0.000010)
3917. feature "SSPN" (0.000010)
3918. feature "CTIF" (0.000010)
3919. feature "RNLS" (0.000010)
3920. feature "DIS3L" (0.000010)
3921. feature "HSBP1L1" (0.000010)
3922. feature "ANKRD34A" (0.000010)
3923. feature "ZNF850" (0.000010)
3924. feature "CDC42EP3" (0.000010)
3925. feature "FHAD1" (0.000010)
3926. feature "TK2" (0.000010)
3927. feature "DOK4" (0.000010)
3928. feature "ZNF772" (0.000010)
3929. feature "RPL10AP6" (0.000010)
3930. feature "PCDH11X" (0.000010)
3931. feature "ADNP-AS1" (0.000010)
3932. feature "RUBCN" (0.000010)
3933. feature "CELSR1" (0.000010)
3934. feature "MAFG" (0.000010)
3935. feature "SNX25" (0.000010)
3936. feature "SMOX" (0.000010)
3937. feature "SEC14L2" (0.000010)
3938. feature "SLC30A6-DT" (0.000010)
3939. feature "KIF9-AS1" (0.000010)
3940. feature "ADORA1" (0.000010)
3941. feature "WDR72" (0.000010)
3942. feature "DGKQ" (0.000010)
3943. feature "DTD2" (0.000009)
3944. feature "SYCE1L" (0.000009)
3945. feature "LINC00960" (0.000009)
3946. feature "HMGCR" (0.000009)
3947. feature "CLCN6" (0.000009)
3948. feature "PECR" (0.000009)
3949. feature "ZNF286B" (0.000009)
3950. feature "SORBS1" (0.000009)
3951. feature "KAT14" (0.000009)
3952. feature "GREB1" (0.000009)
3953. feature "KLK10" (0.000009)
3954. feature "LINC00997" (0.000009)
3955. feature "IL11" (0.000009)
3956. feature "LAX1" (0.000009)
3957. feature "BDKRB2" (0.000009)
3958. feature "TMEM62" (0.000009)
3959. feature "ALDH1A3-AS1" (0.000009)
3960. feature "HPSE" (0.000009)
3961. feature "PLXND1" (0.000009)
3962. feature "MCOLN1" (0.000009)
3963. feature "FSD1L" (0.000009)
3964. feature "KCNJ6" (0.000009)
3965. feature "DOCK8" (0.000009)
3966. feature "TNFRSF14" (0.000009)
3967. feature "MTMR1" (0.000009)
3968. feature "LINC00294" (0.000009)
3969. feature "CYP19A1" (0.000009)
3970. feature "SCD" (0.000009)
3971. feature "FBXL15" (0.000009)
3972. feature "PARM1" (0.000009)
3973. feature "CNKSR1" (0.000009)
3974. feature "RHOU" (0.000009)
3975. feature "MTCO1P40" (0.000009)
3976. feature "MCM10" (0.000009)
3977. feature "ITPRIP" (0.000009)
3978. feature "DIPK1A" (0.000009)
3979. feature "ZNF542P" (0.000009)
3980. feature "FBXO33" (0.000009)
3981. feature "NFKBIA" (0.000009)
3982. feature "SLC25A19" (0.000009)
3983. feature "NTRK2" (0.000009)
3984. feature "B4GALNT2" (0.000009)
3985. feature "CCT6B" (0.000009)
3986. feature "TVP23C" (0.000009)
3987. feature "S100A8" (0.000009)
3988. feature "EVI5L" (0.000009)
3989. feature "C19orf44" (0.000009)
3990. feature "C11orf71" (0.000009)
3991. feature "CDC42BPG" (0.000009)
3992. feature "KCNMB4" (0.000009)
3993. feature "ZNF530" (0.000009)
3994. feature "KDM4C" (0.000009)
3995. feature "ZNF710" (0.000009)
3996. feature "TMEM139" (0.000009)
3997. feature "TPH1" (0.000009)
3998. feature "IL1RAP" (0.000009)
3999. feature "HTR7P1" (0.000009)
4000. feature "GNAO1-DT" (0.000009)
4001. feature "PRAG1" (0.000009)
4002. feature "KRT83" (0.000009)
4003. feature "NARF-AS2" (0.000009)
4004. feature "ACTG2" (0.000009)
4005. feature "CAPRIN2" (0.000009)
4006. feature "NAIP" (0.000009)
4007. feature "TM4SF19-AS1" (0.000009)
4008. feature "SLC19A2" (0.000009)
4009. feature "ZNF391" (0.000009)
4010. feature "NUDT16L2P" (0.000009)
4011. feature "PCDHGB6" (0.000009)
4012. feature "F8" (0.000009)
4013. feature "MYLK-AS1" (0.000009)
4014. feature "PPP1R3C" (0.000009)
4015. feature "PLCD4" (0.000009)
4016. feature "NDN" (0.000009)
4017. feature "C12orf56" (0.000009)
4018. feature "C2CD2" (0.000009)
4019. feature "LY6E-DT" (0.000009)
4020. feature "PID1" (0.000009)
4021. feature "YRDC" (0.000009)
4022. feature "VWA1" (0.000009)
4023. feature "PBLD" (0.000009)
4024. feature "KDM5B" (0.000009)
4025. feature "SLC24A2" (0.000009)
4026. feature "TMEM158" (0.000009)
4027. feature "ZNF319" (0.000009)
4028. feature "SLC25A51P4" (0.000009)
4029. feature "TXNDC5" (0.000009)
4030. feature "CALML3-AS1" (0.000009)
4031. feature "ZNF45" (0.000009)
4032. feature "FOXE1" (0.000009)
4033. feature "ZNF572" (0.000009)
4034. feature "ABAT" (0.000009)
4035. feature "BMP2" (0.000009)
4036. feature "OSER1-DT" (0.000009)
4037. feature "GYG2" (0.000009)
4038. feature "SHFL" (0.000009)
4039. feature "FAM81A" (0.000009)
4040. feature "ACP5" (0.000009)
4041. feature "EVC2" (0.000009)
4042. feature "FOS" (0.000009)
4043. feature "BTG2" (0.000009)
4044. feature "LINC01902" (0.000009)
4045. feature "PIGBOS1" (0.000009)
4046. feature "NFKBID" (0.000009)
4047. feature "NUPR2" (0.000009)
4048. feature "PTCH1" (0.000009)
4049. feature "CTSK" (0.000009)
4050. feature "GNAS-AS1" (0.000009)
4051. feature "ATL1" (0.000009)
4052. feature "ANO7L1" (0.000009)
4053. feature "MAP3K12" (0.000009)
4054. feature "ZKSCAN4" (0.000009)
4055. feature "INKA1" (0.000009)
4056. feature "ZNF383" (0.000009)
4057. feature "DTL" (0.000009)
4058. feature "ITGA6" (0.000009)
4059. feature "FLI1" (0.000009)
4060. feature "ASAH2B" (0.000009)
4061. feature "ZNF416" (0.000009)
4062. feature "STARD4" (0.000009)
4063. feature "HDAC5" (0.000009)
4064. feature "ZNF90P3" (0.000009)
4065. feature "MYO3B-AS1" (0.000009)
4066. feature "RETSAT" (0.000009)
4067. feature "HDAC11" (0.000009)
4068. feature "NPL" (0.000009)
4069. feature "ANKRD13B" (0.000009)
4070. feature "TSHR" (0.000009)
4071. feature "LYSMD1" (0.000009)
4072. feature "ADCY2" (0.000009)
4073. feature "GSDMB" (0.000009)
4074. feature "UPF3AP2" (0.000009)
4075. feature "MRPL20-AS1" (0.000009)
4076. feature "SOX7" (0.000009)
4077. feature "PHC2" (0.000008)
4078. feature "EHHADH" (0.000008)
4079. feature "DPH1" (0.000008)
4080. feature "TMEM218" (0.000008)
4081. feature "MYG1-AS1" (0.000008)
4082. feature "SSR3" (0.000008)
4083. feature "MICB" (0.000008)
4084. feature "C19orf48" (0.000008)
4085. feature "ADGRE1" (0.000008)
4086. feature "G0S2" (0.000008)
4087. feature "POMGNT2" (0.000008)
4088. feature "TTC30A" (0.000008)
4089. feature "ZNF257" (0.000008)
4090. feature "LIMD2" (0.000008)
4091. feature "CPE" (0.000008)
4092. feature "IFIT5" (0.000008)
4093. feature "TMEM191C" (0.000008)
4094. feature "SNURF" (0.000008)
4095. feature "TPRG1" (0.000008)
4096. feature "GPAT3" (0.000008)
4097. feature "STIM2-AS1" (0.000008)
4098. feature "NLRP10" (0.000008)
4099. feature "DNAI4" (0.000008)
4100. feature "ZCCHC24" (0.000008)
4101. feature "ACTG1P25" (0.000008)
4102. feature "TUBG1" (0.000008)
4103. feature "NAT1" (0.000008)
4104. feature "OSGEPL1" (0.000008)
4105. feature "PRDM1" (0.000008)
4106. feature "HMBOX1" (0.000008)
4107. feature "EMP3" (0.000008)
4108. feature "CLHC1" (0.000008)
4109. feature "D2HGDH" (0.000008)
4110. feature "CERS4" (0.000008)
4111. feature "HHEX" (0.000008)
4112. feature "SLC16A4" (0.000008)
4113. feature "CTSD" (0.000008)
4114. feature "TAF6L" (0.000008)
4115. feature "CDKL3" (0.000008)
4116. feature "EDAR" (0.000008)
4117. feature "NRXN3" (0.000008)
4118. feature "VPS45" (0.000008)
4119. feature "SCRG1" (0.000008)
4120. feature "SHOX2" (0.000008)
4121. feature "IL20RB" (0.000008)
4122. feature "EGFLAM" (0.000008)
4123. feature "ZNF304" (0.000008)
4124. feature "HS3ST3B1" (0.000008)
4125. feature "SLC25A21-AS1" (0.000008)
4126. feature "ATP13A4" (0.000008)
4127. feature "FGF8" (0.000008)
4128. feature "STOX1" (0.000008)
4129. feature "TRIB2" (0.000008)
4130. feature "MTND4P12" (0.000008)
4131. feature "RPS12P16" (0.000008)
4132. feature "STON2" (0.000008)
4133. feature "FBXO4" (0.000008)
4134. feature "B3GLCT" (0.000008)
4135. feature "HSPD1P5" (0.000008)
4136. feature "TRIM68" (0.000008)
4137. feature "MOSPD3" (0.000008)
4138. feature "SUSD3" (0.000008)
4139. feature "UCHL1" (0.000008)
4140. feature "TMEM216" (0.000008)
4141. feature "CHAC2" (0.000008)
4142. feature "PLAC9P1" (0.000008)
4143. feature "BRINP2" (0.000008)
4144. feature "FAM86C2P" (0.000008)
4145. feature "OR5AS1" (0.000008)
4146. feature "SLC6A9" (0.000008)
4147. feature "RABGGTA" (0.000008)
4148. feature "ACSS2" (0.000008)
4149. feature "RPL36AP26" (0.000008)
4150. feature "SLC30A2" (0.000008)
4151. feature "HBA1" (0.000008)
4152. feature "R3HDM4" (0.000008)
4153. feature "LINC01087" (0.000008)
4154. feature "HMGN1P26" (0.000008)
4155. feature "GBA" (0.000008)
4156. feature "PNPLA3" (0.000008)
4157. feature "NPTXR" (0.000008)
4158. feature "BEST1" (0.000008)
4159. feature "CDH8" (0.000008)
4160. feature "SH3D21" (0.000008)
4161. feature "TAGAP-AS1" (0.000008)
4162. feature "SAMD10" (0.000008)
4163. feature "ZNF826P" (0.000008)
4164. feature "DNAJC25-GNG10" (0.000008)
4165. feature "RPS18P9" (0.000008)
4166. feature "MYO18B" (0.000008)
4167. feature "RHOBTB1" (0.000008)
4168. feature "LCAT" (0.000008)
4169. feature "RPL34P1" (0.000008)
4170. feature "HDHD5-AS1" (0.000008)
4171. feature "RBBP8NL" (0.000008)
4172. feature "GPR85" (0.000008)
4173. feature "IQCC" (0.000008)
4174. feature "PRR14" (0.000008)
4175. feature "CENPBD1" (0.000008)
4176. feature "SLC2A3" (0.000008)
4177. feature "FADS1" (0.000008)
4178. feature "CSGALNACT1" (0.000008)
4179. feature "YPEL3" (0.000008)
4180. feature "MT-TI" (0.000008)
4181. feature "SGK1" (0.000008)
4182. feature "IRS3P" (0.000008)
4183. feature "CRTC1" (0.000008)
4184. feature "MIR3677HG" (0.000008)
4185. feature "XKR4" (0.000008)
4186. feature "LY6G5C" (0.000008)
4187. feature "MSMO1" (0.000008)
4188. feature "OBSL1" (0.000008)
4189. feature "TAP1" (0.000008)
4190. feature "GARNL3" (0.000008)
4191. feature "OR2L2" (0.000008)
4192. feature "PSKH1" (0.000008)
4193. feature "TESK1" (0.000008)
4194. feature "XDH" (0.000008)
4195. feature "DNAAF11" (0.000008)
4196. feature "CDKN2D" (0.000008)
4197. feature "NECTIN4" (0.000008)
4198. feature "PECAM1" (0.000008)
4199. feature "CHST14" (0.000008)
4200. feature "PITPNM2" (0.000008)
4201. feature "ANKRD9" (0.000008)
4202. feature "MVP" (0.000008)
4203. feature "ARHGEF19" (0.000008)
4204. feature "DTX2" (0.000008)
4205. feature "VASH2" (0.000008)
4206. feature "FAM72D" (0.000008)
4207. feature "FRMD3" (0.000007)
4208. feature "C17orf99" (0.000007)
4209. feature "CLIC3" (0.000007)
4210. feature "ANKRD42" (0.000007)
4211. feature "LINC02245" (0.000007)
4212. feature "CD59" (0.000007)
4213. feature "SLC31A2" (0.000007)
4214. feature "LINC01322" (0.000007)
4215. feature "LINC00513" (0.000007)
4216. feature "PLEKHM3" (0.000007)
4217. feature "CYRIA" (0.000007)
4218. feature "RTEL1-TNFRSF6B" (0.000007)
4219. feature "ZNF470" (0.000007)
4220. feature "KRT18P3" (0.000007)
4221. feature "EEPD1" (0.000007)
4222. feature "ZFP82" (0.000007)
4223. feature "AXIN2" (0.000007)
4224. feature "RAPGEF6" (0.000007)
4225. feature "MTCL1" (0.000007)
4226. feature "KRT86" (0.000007)
4227. feature "ALDH3A1" (0.000007)
4228. feature "IKBKE" (0.000007)
4229. feature "NR2F2-AS1" (0.000007)
4230. feature "STAMBPL1" (0.000007)
4231. feature "COL6A1" (0.000007)
4232. feature "ABCD1" (0.000007)
4233. feature "TRAPPC13" (0.000007)
4234. feature "PKD1L1" (0.000007)
4235. feature "RN7SL471P" (0.000007)
4236. feature "TMEM144" (0.000007)
4237. feature "U2AF1L4" (0.000007)
4238. feature "KLK6" (0.000007)
4239. feature "L3MBTL2-AS1" (0.000007)
4240. feature "EFCAB5" (0.000007)
4241. feature "RPL37P6" (0.000007)
4242. feature "PELI1" (0.000007)
4243. feature "ZNF195" (0.000007)
4244. feature "USP46-DT" (0.000007)
4245. feature "EDA2R" (0.000007)
4246. feature "TBC1D19" (0.000007)
4247. feature "SLC35C1" (0.000007)
4248. feature "OR4F13P" (0.000007)
4249. feature "TMEM179B" (0.000007)
4250. feature "PINK1" (0.000007)
4251. feature "DOCK6" (0.000007)
4252. feature "DHRS3" (0.000007)
4253. feature "CEP68" (0.000007)
4254. feature "INTS9" (0.000007)
4255. feature "LINC02541" (0.000007)
4256. feature "ITGA11" (0.000007)
4257. feature "ARL15" (0.000007)
4258. feature "RGS4" (0.000007)
4259. feature "CLEC2D" (0.000007)
4260. feature "NAP1L1P1" (0.000007)
4261. feature "DENND6B" (0.000007)
4262. feature "CCSER1" (0.000007)
4263. feature "DEGS2" (0.000007)
4264. feature "NRG1" (0.000007)
4265. feature "TMEM231" (0.000007)
4266. feature "ABHD16A" (0.000007)
4267. feature "PCGF2" (0.000007)
4268. feature "PTGES" (0.000007)
4269. feature "HYAL3" (0.000007)
4270. feature "IRAG1" (0.000007)
4271. feature "FAM166C" (0.000007)
4272. feature "MSRB3" (0.000007)
4273. feature "KRT6A" (0.000007)
4274. feature "DENND2D" (0.000007)
4275. feature "PYCR2" (0.000007)
4276. feature "SERPINB9" (0.000007)
4277. feature "SPINK5" (0.000007)
4278. feature "NOL6" (0.000007)
4279. feature "KIAA0895" (0.000007)
4280. feature "PRR19" (0.000007)
4281. feature "ASB7" (0.000007)
4282. feature "LHX6" (0.000007)
4283. feature "KANK1" (0.000007)
4284. feature "C17orf100" (0.000007)
4285. feature "ATP2A1-AS1" (0.000007)
4286. feature "RNF207" (0.000007)
4287. feature "ZNF182" (0.000007)
4288. feature "UNC13B" (0.000007)
4289. feature "RAB11FIP1P1" (0.000007)
4290. feature "PRRX2" (0.000007)
4291. feature "CHST6" (0.000007)
4292. feature "HDAC6" (0.000007)
4293. feature "METAP1D" (0.000007)
4294. feature "CCDC51" (0.000007)
4295. feature "PRRT2" (0.000007)
4296. feature "SDCBP2-AS1" (0.000007)
4297. feature "TTPA" (0.000007)
4298. feature "SNORA11F" (0.000007)
4299. feature "LINC01426" (0.000007)
4300. feature "TMEM147-AS1" (0.000007)
4301. feature "ZNRF2" (0.000007)
4302. feature "TRIT1" (0.000007)
4303. feature "C3orf35" (0.000007)
4304. feature "PINLYP" (0.000007)
4305. feature "RFTN1" (0.000007)
4306. feature "SIM2" (0.000007)
4307. feature "NRXN2" (0.000007)
4308. feature "TMCO6" (0.000007)
4309. feature "CCNE1" (0.000007)
4310. feature "DSTNP2" (0.000007)
4311. feature "ABHD3" (0.000007)
4312. feature "CLYBL" (0.000007)
4313. feature "ANKS6" (0.000007)
4314. feature "SLC8A1" (0.000007)
4315. feature "S100A9" (0.000007)
4316. feature "TMEM115" (0.000007)
4317. feature "PLEKHN1" (0.000007)
4318. feature "SLC38A2" (0.000007)
4319. feature "GASK1B-AS1" (0.000007)
4320. feature "ACADS" (0.000007)
4321. feature "LINC00598" (0.000007)
4322. feature "B3GNT7" (0.000007)
4323. feature "ZNF441" (0.000007)
4324. feature "HMGB1P1" (0.000007)
4325. feature "TP53I13" (0.000007)
4326. feature "SLC43A3" (0.000007)
4327. feature "ROBO1" (0.000007)
4328. feature "PLEKHA8P1" (0.000007)
4329. feature "CSPG4P12" (0.000007)
4330. feature "ZNF341" (0.000007)
4331. feature "ABCA3" (0.000007)
4332. feature "ADAP2" (0.000007)
4333. feature "PRECSIT" (0.000007)
4334. feature "DPRX" (0.000007)
4335. feature "CRB3" (0.000007)
4336. feature "MEX3C" (0.000007)
4337. feature "NEBL-AS1" (0.000007)
4338. feature "AHCYL2" (0.000007)
4339. feature "CNEP1R1" (0.000007)
4340. feature "SLC20A1" (0.000007)
4341. feature "HOXA7" (0.000007)
4342. feature "ZIC5" (0.000007)
4343. feature "PLAGL1" (0.000007)
4344. feature "CCDC40" (0.000007)
4345. feature "PDK2" (0.000007)
4346. feature "RAB11B-AS1" (0.000007)
4347. feature "ZNF213" (0.000007)
4348. feature "TLL1" (0.000007)
4349. feature "SARS1" (0.000007)
4350. feature "DNAJC28" (0.000007)
4351. feature "FGD2" (0.000007)
4352. feature "IYD" (0.000007)
4353. feature "FAM89A" (0.000007)
4354. feature "ITGB2-AS1" (0.000007)
4355. feature "PTPN7" (0.000007)
4356. feature "ZNF969P" (0.000007)
4357. feature "TFPI2-DT" (0.000007)
4358. feature "LINC02664" (0.000007)
4359. feature "YBEY" (0.000007)
4360. feature "PLXNA3" (0.000007)
4361. feature "DDX58" (0.000007)
4362. feature "EXPH5" (0.000007)
4363. feature "ARL14" (0.000007)
4364. feature "CAPN10-DT" (0.000007)
4365. feature "TDRKH-AS1" (0.000007)
4366. feature "IL1RAPL1" (0.000006)
4367. feature "GLB1L" (0.000006)
4368. feature "HDAC4" (0.000006)
4369. feature "CALCOCO1" (0.000006)
4370. feature "PTPN9" (0.000006)
4371. feature "RBM26-AS1" (0.000006)
4372. feature "NR1D1" (0.000006)
4373. feature "C3orf62" (0.000006)
4374. feature "HTR7" (0.000006)
4375. feature "RPL23AP7" (0.000006)
4376. feature "LINC00342" (0.000006)
4377. feature "DUSP12" (0.000006)
4378. feature "GIHCG" (0.000006)
4379. feature "COL19A1" (0.000006)
4380. feature "CDH1" (0.000006)
4381. feature "B3GNT5" (0.000006)
4382. feature "AKR1B1" (0.000006)
4383. feature "LINC01615" (0.000006)
4384. feature "SLC46A1" (0.000006)
4385. feature "HSH2D" (0.000006)
4386. feature "GAREM2" (0.000006)
4387. feature "MAGEA12" (0.000006)
4388. feature "PLCH1" (0.000006)
4389. feature "ANTXR2" (0.000006)
4390. feature "EBP" (0.000006)
4391. feature "TMEM39A" (0.000006)
4392. feature "SMCO4" (0.000006)
4393. feature "C5orf64" (0.000006)
4394. feature "INSYN2B" (0.000006)
4395. feature "DNAAF8" (0.000006)
4396. feature "TRIM45" (0.000006)
4397. feature "COMMD10" (0.000006)
4398. feature "C8orf88" (0.000006)
4399. feature "CHMP1B2P" (0.000006)
4400. feature "KLRK1-AS1" (0.000006)
4401. feature "RASSF10" (0.000006)
4402. feature "LINC01854" (0.000006)
4403. feature "ABTB1" (0.000006)
4404. feature "TRPT1" (0.000006)
4405. feature "ZNF117" (0.000006)
4406. feature "OR7E122P" (0.000006)
4407. feature "NXT2" (0.000006)
4408. feature "TEAD2" (0.000006)
4409. feature "PPIEL" (0.000006)
4410. feature "MMP1" (0.000006)
4411. feature "CFH" (0.000006)
4412. feature "ADGRL2" (0.000006)
4413. feature "KLK5" (0.000006)
4414. feature "ZNF880" (0.000006)
4415. feature "TRANK1" (0.000006)
4416. feature "PRIMPOL" (0.000006)
4417. feature "MFSD4A" (0.000006)
4418. feature "NT5E" (0.000006)
4419. feature "APOL3" (0.000006)
4420. feature "DENND4A" (0.000006)
4421. feature "AGO4" (0.000006)
4422. feature "ZNF555" (0.000006)
4423. feature "TCP11L1" (0.000006)
4424. feature "RINL" (0.000006)
4425. feature "UNC13C" (0.000006)
4426. feature "B4GAT1" (0.000006)
4427. feature "PPCDC" (0.000006)
4428. feature "SLC29A1" (0.000006)
4429. feature "MIF4GD-DT" (0.000006)
4430. feature "FXYD3" (0.000006)
4431. feature "CCDC153" (0.000006)
4432. feature "WWC2" (0.000006)
4433. feature "ZG16B" (0.000006)
4434. feature "TAB1" (0.000006)
4435. feature "KCNK5" (0.000006)
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4502. feature "TMEM79" (0.000006)
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4507. feature "CLIP2" (0.000006)
4508. feature "PLA2G6" (0.000006)
4509. feature "PGBD5" (0.000006)
4510. feature "CACNB4" (0.000006)
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4513. feature "RASGRP2" (0.000006)
4514. feature "NLRC5" (0.000006)
4515. feature "DISC1" (0.000006)
4516. feature "ZNF460" (0.000006)
4517. feature "HARBI1" (0.000006)
4518. feature "ACTN1" (0.000005)
4519. feature "MT1G" (0.000005)
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4524. feature "SLC9A3R1-AS1" (0.000005)
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4531. feature "HHAT" (0.000005)
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4533. feature "ARHGEF10L" (0.000005)
4534. feature "CA4" (0.000005)
4535. feature "LINC01962" (0.000005)
4536. feature "TSPAN33" (0.000005)
4537. feature "EEF1A1P25" (0.000005)
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4539. feature "MMAA" (0.000005)
4540. feature "CCDC141" (0.000005)
4541. feature "ARSL" (0.000005)
4542. feature "RPL5P3" (0.000005)
4543. feature "ZNF687-AS1" (0.000005)
4544. feature "FIBCD1" (0.000005)
4545. feature "STARD13-AS" (0.000005)
4546. feature "CKMT1A" (0.000005)
4547. feature "WDR73" (0.000005)
4548. feature "GALNT18" (0.000005)
4549. feature "SCX" (0.000005)
4550. feature "ZNF512" (0.000005)
4551. feature "ST20-AS1" (0.000005)
4552. feature "SLC36A2" (0.000005)
4553. feature "CPEB2" (0.000005)
4554. feature "OR7E14P" (0.000005)
4555. feature "XAF1" (0.000005)
4556. feature "TTYH1" (0.000005)
4557. feature "JHY" (0.000005)
4558. feature "MORN3" (0.000005)
4559. feature "GPR137B" (0.000005)
4560. feature "LBHD2" (0.000005)
4561. feature "CALR4P" (0.000005)
4562. feature "PPIAP46" (0.000005)
4563. feature "MMP28" (0.000005)
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4565. feature "NR2F2" (0.000005)
4566. feature "WASH6P" (0.000005)
4567. feature "NGEF" (0.000005)
4568. feature "UHRF1" (0.000005)
4569. feature "C20orf204" (0.000005)
4570. feature "LRRC56" (0.000005)
4571. feature "KLHL13" (0.000005)
4572. feature "TUSC1" (0.000005)
4573. feature "CDK6-AS1" (0.000005)
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4575. feature "MPC1-DT" (0.000005)
4576. feature "CORO1A" (0.000005)
4577. feature "MCM3AP-AS1" (0.000005)
4578. feature "PHETA1" (0.000005)
4579. feature "NCF2" (0.000005)
4580. feature "PLA2G4B" (0.000005)
4581. feature "INMT" (0.000005)
4582. feature "LRFN1" (0.000005)
4583. feature "RBMS3" (0.000005)
4584. feature "SMPD4BP" (0.000005)
4585. feature "LINC00271" (0.000005)
4586. feature "LRRC2" (0.000005)
4587. feature "RCOR2" (0.000005)
4588. feature "PRKAA2" (0.000005)
4589. feature "NPIPB4" (0.000005)
4590. feature "ZNF212" (0.000005)
4591. feature "PINK1-AS" (0.000005)
4592. feature "MUC4" (0.000005)
4593. feature "MAPK8IP1" (0.000005)
4594. feature "CCDC69" (0.000005)
4595. feature "PGPEP1" (0.000005)
4596. feature "DLGAP1-AS2" (0.000005)
4597. feature "MIF-AS1" (0.000005)
4598. feature "MALRD1" (0.000005)
4599. feature "C9orf152" (0.000005)
4600. feature "RPL23AP53" (0.000005)
4601. feature "OR10K1" (0.000005)
4602. feature "CNPY4" (0.000005)
4603. feature "ST20" (0.000005)
4604. feature "PTTG1" (0.000005)
4605. feature "BBOF1" (0.000005)
4606. feature "ZNF517" (0.000005)
4607. feature "PSEN2" (0.000005)
4608. feature "MIR663B" (0.000005)
4609. feature "ATP1A4" (0.000005)
4610. feature "ZNF784" (0.000005)
4611. feature "VAV1" (0.000005)
4612. feature "HROB" (0.000005)
4613. feature "ZNF69" (0.000005)
4614. feature "APBB2" (0.000005)
4615. feature "IL10RB" (0.000005)
4616. feature "ADRB2" (0.000005)
4617. feature "CALB2" (0.000005)
4618. feature "ARHGEF34P" (0.000005)
4619. feature "FMC1" (0.000005)
4620. feature "PITX1-AS1" (0.000005)
4621. feature "RBFOX3" (0.000005)
4622. feature "PPP1R15A" (0.000005)
4623. feature "WFDC3" (0.000005)
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4625. feature "MPG" (0.000005)
4626. feature "PMS2CL" (0.000005)
4627. feature "ZBTB18" (0.000005)
4628. feature "F2R" (0.000005)
4629. feature "MPV17L2" (0.000005)
4630. feature "SLC41A3" (0.000005)
4631. feature "KCTD6" (0.000005)
4632. feature "SLCO4A1" (0.000005)
4633. feature "SLC26A6" (0.000005)
4634. feature "P4HB" (0.000005)
4635. feature "NLRC3" (0.000005)
4636. feature "FSTL5" (0.000005)
4637. feature "AMBRA1" (0.000005)
4638. feature "ITIH6" (0.000005)
4639. feature "CCNY-AS1" (0.000005)
4640. feature "FOXD1" (0.000005)
4641. feature "C19orf73" (0.000005)
4642. feature "OXTR" (0.000005)
4643. feature "BDH2" (0.000005)
4644. feature "RNU6-450P" (0.000005)
4645. feature "APLN" (0.000005)
4646. feature "LOX" (0.000005)
4647. feature "FBXO6" (0.000005)
4648. feature "ELAC1" (0.000005)
4649. feature "TICAM1" (0.000005)
4650. feature "SNHG20" (0.000005)
4651. feature "ODAD2" (0.000005)
4652. feature "SMG8" (0.000005)
4653. feature "BDNF-AS" (0.000005)
4654. feature "RNASE7" (0.000005)
4655. feature "SLC47A1" (0.000005)
4656. feature "DUSP5" (0.000005)
4657. feature "HCLS1" (0.000005)
4658. feature "NOP14-AS1" (0.000005)
4659. feature "VEPH1" (0.000005)
4660. feature "RRN3P1" (0.000005)
4661. feature "MT-TK" (0.000005)
4662. feature "ARHGEF33" (0.000005)
4663. feature "MAP7D2" (0.000005)
4664. feature "PLEKHA7" (0.000005)
4665. feature "BTC" (0.000005)
4666. feature "LINC01637" (0.000005)
4667. feature "FBF1" (0.000005)
4668. feature "SPACA6" (0.000005)
4669. feature "FANCI" (0.000005)
4670. feature "SIMC1" (0.000005)
4671. feature "PPP2R5B" (0.000005)
4672. feature "GNRHR2" (0.000005)
4673. feature "LRRC8E" (0.000005)
4674. feature "KLRG2" (0.000005)
4675. feature "CDCA8" (0.000005)
4676. feature "ZNF256" (0.000005)
4677. feature "MAP3K14-AS1" (0.000005)
4678. feature "BLOC1S5-TXNDC5" (0.000005)
4679. feature "REP15" (0.000005)
4680. feature "EFEMP1" (0.000005)
4681. feature "HMGN1P17" (0.000005)
4682. feature "LPAR6" (0.000005)
4683. feature "C1S" (0.000005)
4684. feature "SLITRK2" (0.000005)
4685. feature "LINC02710" (0.000005)
4686. feature "CRACDL" (0.000005)
4687. feature "TMEM201" (0.000005)
4688. feature "SYT7" (0.000005)
4689. feature "FA2H" (0.000005)
4690. feature "LBHD1" (0.000005)
4691. feature "IMPDH1P8" (0.000005)
4692. feature "PDZRN3" (0.000005)
4693. feature "ACSF2" (0.000005)
4694. feature "PGF" (0.000005)
4695. feature "GNG7" (0.000005)
4696. feature "RFLNB" (0.000005)
4697. feature "LINC01275" (0.000005)
4698. feature "MAML1" (0.000005)
4699. feature "S1PR3" (0.000004)
4700. feature "ZSWIM5" (0.000004)
4701. feature "ZNF396" (0.000004)
4702. feature "GABBR2" (0.000004)
4703. feature "KANK2" (0.000004)
4704. feature "FBXO8" (0.000004)
4705. feature "TMEM169" (0.000004)
4706. feature "HEY2" (0.000004)
4707. feature "MT-TF" (0.000004)
4708. feature "EID2B" (0.000004)
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4710. feature "TUBG2" (0.000004)
4711. feature "DTX4" (0.000004)
4712. feature "PIGCP1" (0.000004)
4713. feature "SPRR3" (0.000004)
4714. feature "CDC20" (0.000004)
4715. feature "INAFM2" (0.000004)
4716. feature "ZNF722P" (0.000004)
4717. feature "RPL7AP10" (0.000004)
4718. feature "PDCD6IPP2" (0.000004)
4719. feature "GPRC5D" (0.000004)
4720. feature "HPS6" (0.000004)
4721. feature "WFDC1" (0.000004)
4722. feature "SERPINB7" (0.000004)
4723. feature "HSD11B2" (0.000004)
4724. feature "LINC01679" (0.000004)
4725. feature "TBC1D8" (0.000004)
4726. feature "GFI1" (0.000004)
4727. feature "CRLF1" (0.000004)
4728. feature "SCAMP5" (0.000004)
4729. feature "ENGASE" (0.000004)
4730. feature "ZBTB49" (0.000004)
4731. feature "RND2" (0.000004)
4732. feature "MYO15B" (0.000004)
4733. feature "DPYSL5" (0.000004)
4734. feature "CCPG1" (0.000004)
4735. feature "COPZ2" (0.000004)
4736. feature "RNF185" (0.000004)
4737. feature "ZNF573" (0.000004)
4738. feature "TBX3" (0.000004)
4739. feature "TCAIM" (0.000004)
4740. feature "SHISA4" (0.000004)
4741. feature "IQCH-AS1" (0.000004)
4742. feature "WDR27" (0.000004)
4743. feature "LINC00240" (0.000004)
4744. feature "PIFO" (0.000004)
4745. feature "RAPGEF3" (0.000004)
4746. feature "ST13P7" (0.000004)
4747. feature "TDRD5" (0.000004)
4748. feature "DLG4" (0.000004)
4749. feature "CTSH" (0.000004)
4750. feature "MIF" (0.000004)
4751. feature "NDRG4" (0.000004)
4752. feature "SLAMF9" (0.000004)
4753. feature "BMF" (0.000004)
4754. feature "PTGES3L" (0.000004)
4755. feature "XKR6" (0.000004)
4756. feature "CNDP1" (0.000004)
4757. feature "PPP3CC" (0.000004)
4758. feature "LINC00167" (0.000004)
4759. feature "WDR35" (0.000004)
4760. feature "CCDC183" (0.000004)
4761. feature "ZNF626" (0.000004)
4762. feature "CABP4" (0.000004)
4763. feature "PAX6" (0.000004)
4764. feature "VGLL1" (0.000004)
4765. feature "TM4SF1" (0.000004)
4766. feature "SH2B3" (0.000004)
4767. feature "IMMP2L" (0.000004)
4768. feature "GPRC5C" (0.000004)
4769. feature "ZNF384" (0.000004)
4770. feature "MC2R" (0.000004)
4771. feature "MAP3K8" (0.000004)
4772. feature "EID2" (0.000004)
4773. feature "IFIT2" (0.000004)
4774. feature "DLG5-AS1" (0.000004)
4775. feature "MYH7B" (0.000004)
4776. feature "NDUFA6-DT" (0.000004)
4777. feature "TIAM2" (0.000004)
4778. feature "IPO5P1" (0.000004)
4779. feature "OR2AT4" (0.000004)
4780. feature "AIMP1P1" (0.000004)
4781. feature "RCCD1-AS1" (0.000004)
4782. feature "CNIH3" (0.000004)
4783. feature "LINC01764" (0.000004)
4784. feature "MTND2P28" (0.000004)
4785. feature "PARVB" (0.000004)
4786. feature "NME3" (0.000004)
4787. feature "BMS1P22" (0.000004)
4788. feature "IFRD1" (0.000004)
4789. feature "MAS1" (0.000004)
4790. feature "ZNF154" (0.000004)
4791. feature "PDLIM3" (0.000004)
4792. feature "CRYM-AS1" (0.000004)
4793. feature "ANK1" (0.000004)
4794. feature "SREBF2" (0.000004)
4795. feature "FAM228B" (0.000004)
4796. feature "ZNF417" (0.000004)
4797. feature "RSKR" (0.000004)
4798. feature "KRBOX4" (0.000004)
4799. feature "MYLPF" (0.000004)
4800. feature "RAB9A" (0.000004)
4801. feature "PDE9A" (0.000004)
4802. feature "SLC38A4" (0.000004)
4803. feature "ETS1" (0.000004)
4804. feature "C20orf96" (0.000004)
4805. feature "ATP1B2" (0.000004)
4806. feature "SLC4A10" (0.000004)
4807. feature "CNFN" (0.000004)
4808. feature "TMEM63C" (0.000004)
4809. feature "ZNF420" (0.000004)
4810. feature "FBXO17" (0.000004)
4811. feature "RDH14" (0.000004)
4812. feature "DNLZ" (0.000004)
4813. feature "TPCN2" (0.000004)
4814. feature "LPAR5" (0.000004)
4815. feature "GBP1" (0.000004)
4816. feature "SNORA28" (0.000004)
4817. feature "RAMP3" (0.000004)
4818. feature "SNN" (0.000004)
4819. feature "LINC01551" (0.000004)
4820. feature "LINC01560" (0.000004)
4821. feature "SETMAR" (0.000004)
4822. feature "CHRNG" (0.000004)
4823. feature "SOX17" (0.000004)
4824. feature "CCDC15-DT" (0.000004)
4825. feature "PPFIA3" (0.000004)
4826. feature "SF3A2" (0.000004)
4827. feature "GOLGA2P5" (0.000004)
4828. feature "TNC" (0.000004)
4829. feature "VPS37D" (0.000004)
4830. feature "RASAL1" (0.000004)
4831. feature "FOXO6" (0.000004)
4832. feature "MCMDC2" (0.000004)
4833. feature "MYO18A" (0.000004)
4834. feature "SLC9A3" (0.000004)
4835. feature "PAQR9-AS1" (0.000004)
4836. feature "OTUB2" (0.000004)
4837. feature "FHIP1A" (0.000004)
4838. feature "TRIM31" (0.000004)
4839. feature "RPL12P25" (0.000004)
4840. feature "PEAR1" (0.000004)
4841. feature "GRAMD4P3" (0.000004)
4842. feature "BBS4" (0.000004)
4843. feature "RGL3" (0.000004)
4844. feature "NXN" (0.000004)
4845. feature "PLAAT4" (0.000004)
4846. feature "FGF22" (0.000004)
4847. feature "ADAM19" (0.000004)
4848. feature "GPRC5D-AS1" (0.000004)
4849. feature "BATF3" (0.000004)
4850. feature "RPS6P26" (0.000004)
4851. feature "CYB561D1" (0.000004)
4852. feature "LINC00672" (0.000004)
4853. feature "LRRC37A" (0.000004)
4854. feature "NIBAN3" (0.000004)
4855. feature "EHD3" (0.000004)
4856. feature "FGFR2" (0.000004)
4857. feature "CYP2J2" (0.000004)
4858. feature "SERPINB9P1" (0.000004)
4859. feature "LRCH4" (0.000004)
4860. feature "DEPP1" (0.000004)
4861. feature "ZNF81" (0.000004)
4862. feature "L3HYPDH" (0.000004)
4863. feature "ARL6" (0.000004)
4864. feature "ALDH1A3" (0.000004)
4865. feature "FN1" (0.000004)
4866. feature "PTPRN" (0.000004)
4867. feature "QPCTL" (0.000004)
4868. feature "FOXO3B" (0.000004)
4869. feature "YWHAEP5" (0.000004)
4870. feature "DLX2" (0.000004)
4871. feature "AGK" (0.000004)
4872. feature "HERC3" (0.000004)
4873. feature "INPP1" (0.000004)
4874. feature "PSD3" (0.000004)
4875. feature "MIR194-2HG" (0.000004)
4876. feature "GLIPR1" (0.000004)
4877. feature "CRY2" (0.000004)
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4880. feature "YBX1P6" (0.000004)
4881. feature "KRTAP20-4" (0.000004)
4882. feature "ISOC1" (0.000004)
4883. feature "MCM7" (0.000004)
4884. feature "HOXC6" (0.000004)
4885. feature "ACTR1B" (0.000004)
4886. feature "MAP3K2-DT" (0.000004)
4887. feature "TAT-AS1" (0.000004)
4888. feature "TRIM3" (0.000004)
4889. feature "BORA" (0.000003)
4890. feature "LRTM1" (0.000003)
4891. feature "WRAP73" (0.000003)
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8288. feature "CYP26C1" (0.000000)
8289. feature "ANO9" (0.000000)
8290. feature "CXCL2" (0.000000)
8291. feature "DNAJB5" (0.000000)
8292. feature "ARRDC4" (0.000000)
8293. feature "MAG" (0.000000)
8294. feature "THSD7A" (0.000000)
8295. feature "ARRDC3-AS1" (0.000000)
8296. feature "CD99L2" (0.000000)
8297. feature "IL17RB" (0.000000)
8298. feature "F2RL3" (0.000000)
8299. feature "LINC00184" (0.000000)
8300. feature "GABRQ" (0.000000)
8301. feature "CECR2" (0.000000)
8302. feature "GREM2" (0.000000)
8303. feature "TMEM74B" (0.000000)
8304. feature "C10orf143" (0.000000)
8305. feature "ZNF805" (0.000000)
8306. feature "ZNF665" (0.000000)
8307. feature "CPQ" (0.000000)
8308. feature "NOTCH3" (0.000000)
8309. feature "LIPA" (0.000000)
8310. feature "GABRD" (0.000000)
8311. feature "ZNF610" (0.000000)
8312. feature "ZNF415" (0.000000)
8313. feature "POU3F4" (0.000000)
8314. feature "SYT3" (0.000000)
8315. feature "C16orf86" (0.000000)
8316. feature "RIPK3" (0.000000)
8317. feature "ACADL" (0.000000)
8318. feature "ABCA6" (0.000000)
8319. feature "TTC23L-AS1" (0.000000)
8320. feature "NKX6-1" (0.000000)
8321. feature "COBL" (0.000000)
8322. feature "SLC30A3" (0.000000)
8323. feature "IGFBP1" (0.000000)
8324. feature "ZNF311" (0.000000)
8325. feature "PDE2A" (0.000000)
8326. feature "LINC00235" (0.000000)
8327. feature "TMEM187" (0.000000)
8328. feature "ZNF563" (0.000000)
8329. feature "ENPP5" (0.000000)
8330. feature "LINC01843" (0.000000)
8331. feature "C7" (0.000000)
8332. feature "PPM1J" (0.000000)
8333. feature "FAM25A" (0.000000)
8334. feature "ZNF737" (0.000000)
8335. feature "FEZF1" (0.000000)
8336. feature "RP1L1" (0.000000)
8337. feature "GLDC" (0.000000)
8338. feature "MYH3" (0.000000)
8339. feature "GLIPR2" (0.000000)
8340. feature "ZNF697" (0.000000)
8341. feature "LINC02065" (0.000000)
8342. feature "IL2RG" (0.000000)
8343. feature "LINC02701" (0.000000)
8344. feature "ZNF775" (0.000000)
8345. feature "RPSAP41" (0.000000)
8346. feature "TNIP3" (0.000000)
8347. feature "RNF152" (0.000000)
8348. feature "SIDT2" (0.000000)
8349. feature "NOXA1" (0.000000)
8350. feature "PCDHB6" (0.000000)
8351. feature "USP27X" (0.000000)
8352. feature "BMP6" (0.000000)
8353. feature "KCNN3" (0.000000)
8354. feature "TNFRSF1B" (0.000000)
8355. feature "LINC00858" (0.000000)
8356. feature "PRSS21" (0.000000)
8357. feature "IL12RB2" (0.000000)
8358. feature "TAGLN" (0.000000)
8359. feature "TSPAN8" (0.000000)
8360. feature "SPOCK2" (0.000000)
8361. feature "CD86" (0.000000)
8362. feature "DMRTA1" (0.000000)
8363. feature "ACKR2" (0.000000)
8364. feature "KCTD8" (0.000000)
8365. feature "CPLANE2" (0.000000)
8366. feature "IL1RL1" (0.000000)
8367. feature "SDK1" (0.000000)
8368. feature "SLCO4C1" (0.000000)
8369. feature "NTF3" (0.000000)
8370. feature "SPON2" (0.000000)
8371. feature "CNR1" (0.000000)
8372. feature "ALOX5AP" (0.000000)
8373. feature "SHROOM4" (0.000000)
8374. feature "LRRIQ4" (0.000000)
8375. feature "ANKRD31" (0.000000)
8376. feature "LINC02635" (0.000000)
8377. feature "ZNF225" (0.000000)
8378. feature "QRFPR" (0.000000)
8379. feature "SPOCK3" (0.000000)
8380. feature "TNFSF14" (0.000000)
8381. feature "HTR2C" (0.000000)
8382. feature "KCNQ2" (0.000000)
8383. feature "GIMAP2" (0.000000)
8384. feature "KRCC1" (0.000000)
8385. feature "IMPDH1P3" (0.000000)
8386. feature "CYP2R1" (0.000000)
8387. feature "CNRIP1" (0.000000)
8388. feature "LINC02709" (0.000000)
8389. feature "NWD1" (0.000000)
8390. feature "P4HTM" (0.000000)
8391. feature "LINC01819" (0.000000)
8392. feature "FAM218A" (0.000000)
8393. feature "GFPT1" (0.000000)
8394. feature "MUC7" (0.000000)
8395. feature "LUM" (0.000000)
8396. feature "ZNF75D" (0.000000)
8397. feature "ZNF185" (0.000000)
8398. feature "PI3" (0.000000)
8399. feature "ACTBL2" (0.000000)
8400. feature "KCNH5" (0.000000)
8401. feature "ZNF565" (0.000000)
8402. feature "CCDC85A" (0.000000)
8403. feature "CNTNAP1" (0.000000)
8404. feature "RDH12" (0.000000)
8405. feature "SHPK" (0.000000)
8406. feature "RTKN" (0.000000)
8407. feature "CTC1" (0.000000)
8408. feature "KRT18P46" (0.000000)
8409. feature "SIPA1" (0.000000)
8410. feature "LMBR1L" (0.000000)
8411. feature "HOXD11" (0.000000)
8412. feature "KDM8" (0.000000)
8413. feature "IGHEP2" (0.000000)
8414. feature "IGF2BP1" (0.000000)
8415. feature "LRGUK" (0.000000)
8416. feature "CFAP65" (0.000000)
8417. feature "ZFYVE26" (0.000000)
8418. feature "SHROOM1" (0.000000)
8419. feature "COL18A1" (0.000000)
8420. feature "PAX3" (0.000000)
8421. feature "FANCD2" (0.000000)
8422. feature "PLXNB1" (0.000000)
8423. feature "HOXD10" (0.000000)
8424. feature "FREM2" (0.000000)
8425. feature "IL12A" (0.000000)
8426. feature "PRKCZ-AS1" (0.000000)
8427. feature "DDR2" (0.000000)
8428. feature "ABHD14A-ACY1" (0.000000)
8429. feature "EPHA6" (0.000000)
8430. feature "PAQR6" (0.000000)
8431. feature "MYO1E" (0.000000)
8432. feature "NCMAP" (0.000000)
8433. feature "SHC3" (0.000000)
8434. feature "SDCBP2" (0.000000)
8435. feature "AMPD3" (0.000000)
8436. feature "CFTR" (0.000000)
8437. feature "PM20D1" (0.000000)
8438. feature "ZNF143" (0.000000)
8439. feature "LINC02861" (0.000000)
8440. feature "RYR3" (0.000000)
8441. feature "OR7E91P" (0.000000)
8442. feature "IMPDH1P5" (0.000000)
8443. feature "LINC00570" (0.000000)
8444. feature "XKR8" (0.000000)
8445. feature "TMEM255B" (0.000000)
8446. feature "CZIB-DT" (0.000000)
8447. feature "CCDC33" (0.000000)
8448. feature "ZNF84" (0.000000)
8449. feature "GPHA2" (0.000000)
8450. feature "BCL2L14" (0.000000)
8451. feature "ZSCAN12P1" (0.000000)
8452. feature "FTH1P24" (0.000000)
8453. feature "SOWAHB" (0.000000)
8454. feature "BDNF" (0.000000)
8455. feature "ST3GAL5" (0.000000)
8456. feature "LINC00607" (0.000000)
8457. feature "H6PD" (0.000000)
8458. feature "LINC00887" (0.000000)
8459. feature "IGSF9" (0.000000)
8460. feature "SLC37A4" (0.000000)
8461. feature "TACR2" (0.000000)
8462. feature "WDR4" (0.000000)
8463. feature "KCNN2" (0.000000)
8464. feature "CSMD3" (0.000000)
8465. feature "RNF224" (0.000000)
8466. feature "BCL2L11" (0.000000)
8467. feature "ZNF551" (0.000000)
8468. feature "USP51" (0.000000)
8469. feature "COL6A2" (0.000000)
8470. feature "ABCC1" (0.000000)
8471. feature "ERBB3" (0.000000)
8472. feature "VPS13C-DT" (0.000000)
8473. feature "MMRN2" (0.000000)
8474. feature "IRX3" (0.000000)
8475. feature "ACAT2" (0.000000)
8476. feature "TLE6" (0.000000)
8477. feature "MXD4" (0.000000)
8478. feature "LINC01237" (0.000000)
8479. feature "ZNF587P1" (0.000000)
8480. feature "FERMT1" (0.000000)
8481. feature "TNFAIP8L3" (0.000000)
8482. feature "C1orf216" (0.000000)
8483. feature "TMPPE" (0.000000)
8484. feature "RNA5SP155" (0.000000)
8485. feature "HSPA7" (0.000000)
8486. feature "TENT5A" (0.000000)
8487. feature "LDB3" (0.000000)
8488. feature "KCNK12" (0.000000)
8489. feature "POU5F1B" (0.000000)
8490. feature "SLC12A1" (0.000000)
8491. feature "HSPA1L" (0.000000)
8492. feature "THBS1-IT1" (0.000000)
8493. feature "LYG1" (0.000000)
8494. feature "SMPDL3B" (0.000000)
8495. feature "LHX4" (0.000000)
8496. feature "PLBD1" (0.000000)
8497. feature "NT5CP1" (0.000000)
8498. feature "PFKFB1" (0.000000)
8499. feature "IFT172" (0.000000)
8500. feature "ADHFE1" (0.000000)
8501. feature "SEMA4F" (0.000000)
8502. feature "NECTIN3-AS1" (0.000000)
8503. feature "THOP1" (0.000000)
8504. feature "PGAM1P7" (0.000000)
8505. feature "PGR" (0.000000)
8506. feature "ARMH1" (0.000000)
8507. feature "EEF1GP4" (0.000000)
8508. feature "FAM227A" (0.000000)
8509. feature "ZNF792" (0.000000)
8510. feature "LINC02716" (0.000000)
8511. feature "LINC00862" (0.000000)
8512. feature "MCC" (0.000000)
8513. feature "RASL11B" (0.000000)
8514. feature "MCAM" (0.000000)
8515. feature "PGGHG" (0.000000)
8516. feature "PRPF19-DT" (0.000000)
8517. feature "LINC02613" (0.000000)
8518. feature "SLC4A3" (0.000000)
8519. feature "OR8G3P" (0.000000)
8520. feature "SNED1" (0.000000)
8521. feature "AKR7A3" (0.000000)
8522. feature "H1-6" (0.000000)
8523. feature "TMEM37" (0.000000)
8524. feature "SLC27A2" (0.000000)
8525. feature "PDE4B" (0.000000)
8526. feature "ANPEP" (0.000000)
8527. feature "SERPINB3" (0.000000)
8528. feature "ADIRF-AS1" (0.000000)
8529. feature "HHIPL1" (0.000000)
8530. feature "GNE" (0.000000)
8531. feature "ITGAM" (0.000000)
8532. feature "SLC6A2" (0.000000)
8533. feature "C3" (0.000000)
8534. feature "CDYL2" (0.000000)
8535. feature "DHX58" (0.000000)
8536. feature "AEBP1" (0.000000)
8537. feature "ARL11" (0.000000)
8538. feature "ZNF229" (0.000000)
8539. feature "ENPP2" (0.000000)
8540. feature "CLDN10" (0.000000)
8541. feature "CHST2" (0.000000)
8542. feature "TSPAN7" (0.000000)
8543. feature "CYP4F11" (0.000000)
8544. feature "TNFRSF8" (0.000000)
8545. feature "WTAPP1" (0.000000)
8546. feature "IL22RA1" (0.000000)
8547. feature "CYP4F12" (0.000000)
8548. feature "TNFSF18" (0.000000)
8549. feature "MYO7B" (0.000000)
8550. feature "LCK" (0.000000)
8551. feature "TCN1" (0.000000)
8552. feature "FABP4" (0.000000)
8553. feature "ESX1" (0.000000)
8554. feature "GJB4" (0.000000)
8555. feature "TEKT3" (0.000000)
8556. feature "SV2A" (0.000000)
8557. feature "PROM1" (0.000000)
8558. feature "IDO1" (0.000000)
8559. feature "CERS3" (0.000000)
8560. feature "ENPP1" (0.000000)
8561. feature "TENM2" (0.000000)
8562. feature "SCG2" (0.000000)
8563. feature "PTPRO" (0.000000)
8564. feature "FAM180A" (0.000000)
8565. feature "NEO1" (0.000000)
8566. feature "ERVFRD-1" (0.000000)
8567. feature "SLC24A1" (0.000000)
8568. feature "LINC01537" (0.000000)
8569. feature "GFPT2" (0.000000)
8570. feature "ZNF844" (0.000000)
8571. feature "FGF19" (0.000000)
8572. feature "SERPINB13" (0.000000)
8573. feature "SNPH" (0.000000)
8574. feature "CCBE1" (0.000000)
8575. feature "F2RL2" (0.000000)
8576. feature "UBA7" (0.000000)
8577. feature "SUSD4" (0.000000)
8578. feature "MATN1-AS1" (0.000000)
8579. feature "LINC01018" (0.000000)
8580. feature "RNF128" (0.000000)
8581. feature "CDH23" (0.000000)
8582. feature "FBLN5" (0.000000)
8583. feature "STK32B" (0.000000)
8584. feature "MUC20" (0.000000)
8585. feature "LVRN" (0.000000)
8586. feature "GJB6" (0.000000)
8587. feature "TRBC2" (0.000000)
8588. feature "PIP5K1B" (0.000000)
8589. feature "AKNA" (0.000000)
8590. feature "EEF1DP3" (0.000000)
8591. feature "MN1" (0.000000)
8592. feature "CELF6" (0.000000)
8593. feature "CCL27" (0.000000)
8594. feature "TSPYL2" (0.000000)
8595. feature "EPHB1" (0.000000)
8596. feature "TMEM163" (0.000000)
8597. feature "LMOD1" (0.000000)
8598. feature "RTL5" (0.000000)
8599. feature "TESC" (0.000000)
8600. feature "AMTN" (0.000000)
8601. feature "TRIM34" (0.000000)
8602. feature "CLDN23" (0.000000)
8603. feature "SMOC2" (0.000000)
8604. feature "TEDC1" (0.000000)
8605. feature "PLA2G4C" (0.000000)
8606. feature "NUDT11" (0.000000)
8607. feature "HRH3" (0.000000)
8608. feature "ADAMTS17" (0.000000)
8609. feature "ANKRD22" (0.000000)
8610. feature "ZMYND12" (0.000000)
8611. feature "ARL4C" (0.000000)
8612. feature "IARS1" (0.000000)
8613. feature "LY6K" (0.000000)
8614. feature "ABLIM3" (0.000000)
8615. feature "TMEM204" (0.000000)
8616. feature "WNT3" (0.000000)
8617. feature "PIANP" (0.000000)
8618. feature "GJB2" (0.000000)
8619. feature "THY1" (0.000000)
8620. feature "CLCA2" (0.000000)
8621. feature "SERPINB10" (0.000000)
8622. feature "CYP4F3" (0.000000)
8623. feature "HEPHL1" (0.000000)
8624. feature "SCEL" (0.000000)
8625. feature "SDHA" (0.000000)
8626. feature "ARSI" (0.000000)
8627. feature "MMP10" (0.000000)
8628. feature "MEST" (0.000000)
8629. feature "VIM" (0.000000)
8630. feature "CLDN8" (0.000000)
8631. feature "VILL" (0.000000)
8632. feature "CD27-AS1" (0.000000)
8633. feature "CCNB3" (0.000000)
8634. feature "SERPINB2" (0.000000)
8635. feature "ALDH1A1" (0.000000)
8636. feature "MAGEC2" (0.000000)
8637. feature "DHRS9" (0.000000)
8638. feature "LRRC38" (0.000000)
8639. feature "FAM13A-AS1" (0.000000)
8640. feature "CDK5R1" (0.000000)
8641. feature "DENND5A" (0.000000)
8642. feature "SLC25A27" (0.000000)
8643. feature "GRIK5" (0.000000)
8644. feature "DNAI7" (0.000000)
8645. feature "FAAHP1" (0.000000)
¶
4.6.3) XGBoost
Also in this case we built the Xgboost classifier.
We exploited its property of running faster to increase the number of hyperparameters for the estimators.
The results were good as this general model achieves a performance of 96% on the test set, that we consider as outstanding.
#i build the XGBoost classifier for the general dataset
from xgboost import XGBClassifier
# Define the XGBoost classifier
xgb = XGBClassifier(random_state=42)
# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [50, 100], 'max_depth': [30, 50]}
grid_search = GridSearchCV(xgb, param_grid, cv=5)
outer_cv = KFold(n_splits=2, shuffle=True, random_state=42)
# Perform the cross validation
scores = cross_val_score(grid_search, X_train_all, y_train_all, cv=outer_cv)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))
# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
grid_search.fit(X_train_all, y_train_all)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)
#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_all, y_test_all))
# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_all.shape[1]):
print("%d. feature %s (%f)" % (f + 1, X_train_all.columns[indices[f]], importances[indices[f]]))
best_xgb_all = grid_search.best_estimator_
Mean accuracy of nested cross validation: 0.9561007854421029
Best hyperparameters: {'max_depth': 50, 'n_estimators': 100}
Best accuracy: 0.95975575976527
Best model: XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=50, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=100, n_jobs=None,
num_parallel_tree=None, random_state=42, ...)
Test accuracy: 0.9608056614044638
Feature ranking:
1. feature "TMSB10" (0.036074)
2. feature "NDRG1" (0.030002)
3. feature "PGK1" (0.014176)
4. feature "IGFBP3" (0.008976)
5. feature "KRT19" (0.007397)
6. feature "GAPDH" (0.007392)
7. feature "BCYRN1" (0.006153)
8. feature "CCDC88A" (0.005787)
9. feature "TFF1" (0.005495)
10. feature "RPL41" (0.005456)
11. feature "LGALS1" (0.005203)
12. feature "MT-RNR2" (0.005140)
13. feature "BNIP3" (0.004985)
14. feature "CHAF1A" (0.004796)
15. feature "AATF" (0.004676)
16. feature "MT-TP" (0.004099)
17. feature "GNAQ" (0.004074)
18. feature "MCM6" (0.003944)
19. feature "JAK1" (0.003754)
20. feature "P4HA1" (0.003651)
21. feature "SLC6A8" (0.003547)
22. feature "LIMCH1" (0.003414)
23. feature "COMMD6" (0.003262)
24. feature "AIP" (0.003238)
25. feature "AKR1C1" (0.003200)
26. feature "NAA50" (0.003135)
27. feature "FNIP1" (0.003102)
28. feature "PRPSAP2" (0.003101)
29. feature "PHRF1" (0.003056)
30. feature "NIBAN2" (0.002948)
31. feature "EGLN3" (0.002941)
32. feature "LDHA" (0.002843)
33. feature "MT-RNR1" (0.002842)
34. feature "LINC02367" (0.002840)
35. feature "CA9" (0.002812)
36. feature "OTULIN" (0.002681)
37. feature "HNRNPA1" (0.002666)
38. feature "DKK1" (0.002664)
39. feature "KMT5B" (0.002660)
40. feature "ZNF33B" (0.002640)
41. feature "MT-ND3" (0.002621)
42. feature "ZFC3H1" (0.002589)
43. feature "MAD2L2" (0.002586)
44. feature "LDHB" (0.002580)
45. feature "STARD7" (0.002563)
46. feature "PUSL1" (0.002495)
47. feature "BNIP3L" (0.002435)
48. feature "TOB1" (0.002383)
49. feature "PCDHGA10" (0.002370)
50. feature "RAD21" (0.002351)
51. feature "MT1E" (0.002305)
52. feature "CTBP2" (0.002299)
53. feature "EBAG9" (0.002281)
54. feature "TNIP2" (0.002270)
55. feature "SOCS4" (0.002261)
56. feature "PCBD1" (0.002252)
57. feature "ENO1" (0.002226)
58. feature "LSM14A" (0.002218)
59. feature "MT-CYB" (0.002207)
60. feature "CIRBP" (0.002199)
61. feature "ZNF121" (0.002196)
62. feature "FAM111B" (0.002195)
63. feature "TMEM64" (0.002191)
64. feature "BRMS1" (0.002155)
65. feature "C8orf58" (0.002142)
66. feature "GNB1" (0.002113)
67. feature "RPS26" (0.002099)
68. feature "PRRC2A" (0.002074)
69. feature "TASOR" (0.002068)
70. feature "AKR1C2" (0.002057)
71. feature "PTOV1" (0.002043)
72. feature "SDHB" (0.002037)
73. feature "CDK2AP2" (0.002037)
74. feature "CD47" (0.002034)
75. feature "NUMA1" (0.002030)
76. feature "CENPB" (0.002019)
77. feature "PRR11" (0.002005)
78. feature "MCL1" (0.002002)
79. feature "UBE2Q1" (0.001985)
80. feature "MLLT3" (0.001973)
81. feature "BMPR1B" (0.001968)
82. feature "UGCG" (0.001960)
83. feature "VIT" (0.001957)
84. feature "NR2F6" (0.001947)
85. feature "KLC2" (0.001939)
86. feature "CADM1" (0.001939)
87. feature "PLOD2" (0.001908)
88. feature "MRPL55" (0.001906)
89. feature "LTBR" (0.001873)
90. feature "L3MBTL2" (0.001868)
91. feature "S100A10" (0.001850)
92. feature "EFNA2" (0.001847)
93. feature "PHACTR1" (0.001846)
94. feature "MAPK14" (0.001844)
95. feature "GOLGA3" (0.001802)
96. feature "SET" (0.001782)
97. feature "RAB12" (0.001779)
98. feature "B4GALT1" (0.001775)
99. feature "RPLP0P2" (0.001758)
100. feature "TPI1" (0.001750)
101. feature "SRM" (0.001727)
102. feature "RBBP8" (0.001705)
103. feature "MARK2" (0.001671)
104. feature "KPNA4" (0.001669)
105. feature "ISOC2" (0.001619)
106. feature "ACTB" (0.001619)
107. feature "PDXK" (0.001613)
108. feature "EIF3J" (0.001609)
109. feature "RPS5" (0.001604)
110. feature "MT-ND4L" (0.001596)
111. feature "MDFI" (0.001560)
112. feature "SENP7" (0.001559)
113. feature "C2CD2L" (0.001554)
114. feature "PLAAT3" (0.001551)
115. feature "DEDD" (0.001543)
116. feature "H3C2" (0.001538)
117. feature "DYNLL2" (0.001535)
118. feature "GDF15" (0.001507)
119. feature "H3-3A" (0.001500)
120. feature "SELENOH" (0.001496)
121. feature "YKT6" (0.001496)
122. feature "PGAP6" (0.001491)
123. feature "MSANTD3" (0.001487)
124. feature "MT-ATP6" (0.001483)
125. feature "PCYT1B" (0.001478)
126. feature "LRRC59" (0.001447)
127. feature "C6orf132" (0.001445)
128. feature "UBE2E1" (0.001437)
129. feature "H2AZ2" (0.001436)
130. feature "SNRNP70" (0.001436)
131. feature "PPFIA1" (0.001435)
132. feature "POLR2I" (0.001432)
133. feature "PAPSS2" (0.001431)
134. feature "FKBP5" (0.001430)
135. feature "BACH1" (0.001426)
136. feature "PKN1" (0.001416)
137. feature "MT-ND4" (0.001412)
138. feature "ATXN2L" (0.001410)
139. feature "ADARB1" (0.001407)
140. feature "KIFC3" (0.001397)
141. feature "VAT1" (0.001392)
142. feature "MT-ND1" (0.001388)
143. feature "DDIT4" (0.001384)
144. feature "MARVELD1" (0.001383)
145. feature "WDR43" (0.001383)
146. feature "HSP90AA1" (0.001371)
147. feature "ANKIB1" (0.001369)
148. feature "MT-ND6" (0.001367)
149. feature "HSPA2" (0.001364)
150. feature "DDX28" (0.001364)
151. feature "EPC2" (0.001364)
152. feature "PKM" (0.001363)
153. feature "XPNPEP1" (0.001359)
154. feature "GIPC1" (0.001352)
155. feature "SUPT5H" (0.001349)
156. feature "RPS19" (0.001349)
157. feature "KRT17" (0.001348)
158. feature "UPF3B" (0.001345)
159. feature "KYNU" (0.001344)
160. feature "MAD2L1" (0.001343)
161. feature "MAP3K13" (0.001339)
162. feature "NMT2" (0.001338)
163. feature "ISCU" (0.001337)
164. feature "STARD10" (0.001335)
165. feature "SLC1A5" (0.001333)
166. feature "ZNF688" (0.001331)
167. feature "GYS1" (0.001325)
168. feature "DCAF7" (0.001319)
169. feature "NEDD1" (0.001317)
170. feature "HSPD1" (0.001316)
171. feature "ARL13B" (0.001314)
172. feature "GDI1" (0.001309)
173. feature "FGF23" (0.001305)
174. feature "TPM1" (0.001298)
175. feature "SHKBP1" (0.001297)
176. feature "MALAT1" (0.001297)
177. feature "USP7" (0.001286)
178. feature "HES1" (0.001285)
179. feature "C7orf50" (0.001284)
180. feature "CDC42BPA" (0.001284)
181. feature "ZFAS1" (0.001282)
182. feature "GPM6A" (0.001282)
183. feature "RPL34" (0.001280)
184. feature "RALBP1" (0.001279)
185. feature "DGUOK-AS1" (0.001278)
186. feature "NDUFB4" (0.001277)
187. feature "PUS7" (0.001272)
188. feature "TTLL5" (0.001271)
189. feature "RHNO1" (0.001266)
190. feature "PTPN11" (0.001256)
191. feature "SH3RF1" (0.001256)
192. feature "MT-TV" (0.001253)
193. feature "TCF20" (0.001246)
194. feature "PCNX2" (0.001245)
195. feature "MRPS33" (0.001244)
196. feature "GNAS" (0.001244)
197. feature "LINC01551" (0.001243)
198. feature "PLSCR1" (0.001240)
199. feature "NRIP1" (0.001240)
200. feature "IRX2" (0.001238)
201. feature "MTND1P23" (0.001237)
202. feature "BRD7" (0.001237)
203. feature "CHD8" (0.001235)
204. feature "ZNF316" (0.001232)
205. feature "AURKA" (0.001231)
206. feature "RPL21" (0.001225)
207. feature "EGR1" (0.001222)
208. feature "HK1" (0.001222)
209. feature "RPS14" (0.001221)
210. feature "MGRN1" (0.001221)
211. feature "TMSB4XP4" (0.001220)
212. feature "OBSCN" (0.001215)
213. feature "RPL39" (0.001214)
214. feature "MDM4" (0.001213)
215. feature "CEP104" (0.001211)
216. feature "AVPI1" (0.001208)
217. feature "TPT1" (0.001206)
218. feature "MT-TS1" (0.001206)
219. feature "ODF2" (0.001203)
220. feature "RPS21" (0.001200)
221. feature "SYT14" (0.001200)
222. feature "C4orf3" (0.001199)
223. feature "ARIH1" (0.001198)
224. feature "POLR2A" (0.001197)
225. feature "EPS8L2" (0.001182)
226. feature "ENAH" (0.001175)
227. feature "STRBP" (0.001173)
228. feature "CARMIL1" (0.001168)
229. feature "RRS1" (0.001164)
230. feature "C12orf75" (0.001157)
231. feature "NORAD" (0.001152)
232. feature "BATF" (0.001150)
233. feature "ANAPC16" (0.001150)
234. feature "FLNA" (0.001143)
235. feature "CAP1" (0.001142)
236. feature "MIR663AHG" (0.001141)
237. feature "LRRFIP2" (0.001139)
238. feature "AMFR" (0.001137)
239. feature "GADD45GIP1" (0.001136)
240. feature "RPL30" (0.001135)
241. feature "NSMF" (0.001134)
242. feature "G6PD" (0.001132)
243. feature "CFAP36" (0.001126)
244. feature "MNS1" (0.001119)
245. feature "RPL10" (0.001116)
246. feature "IGFL2-AS1" (0.001113)
247. feature "AXL" (0.001112)
248. feature "RPL18A" (0.001111)
249. feature "KIAA1522" (0.001111)
250. feature "GSPT1" (0.001105)
251. feature "SAMD15" (0.001102)
252. feature "PFDN4" (0.001102)
253. feature "UBA52" (0.001099)
254. feature "CEP83" (0.001092)
255. feature "MRFAP1" (0.001091)
256. feature "GDPGP1" (0.001089)
257. feature "KMT2D" (0.001089)
258. feature "GARS1" (0.001079)
259. feature "SSX2IP" (0.001078)
260. feature "SLC25A25-AS1" (0.001076)
261. feature "SMG7" (0.001074)
262. feature "MT-CO3" (0.001068)
263. feature "HMGN3" (0.001067)
264. feature "RHOA" (0.001065)
265. feature "PHF12" (0.001062)
266. feature "ZFP36L1" (0.001060)
267. feature "YTHDC1" (0.001057)
268. feature "KAT7" (0.001057)
269. feature "C20orf27" (0.001057)
270. feature "UTP18" (0.001057)
271. feature "CBX4" (0.001053)
272. feature "ZCCHC9" (0.001053)
273. feature "TNKS1BP1" (0.001051)
274. feature "KCNJ3" (0.001048)
275. feature "SVIP" (0.001044)
276. feature "FAM222B" (0.001041)
277. feature "TSPYL1" (0.001040)
278. feature "EIF5AL1" (0.001040)
279. feature "ELP2" (0.001032)
280. feature "PSAP" (0.001031)
281. feature "C19orf53" (0.001030)
282. feature "KIF16B" (0.001021)
283. feature "NFE2L2" (0.001019)
284. feature "MYBL1" (0.001018)
285. feature "SPACA6" (0.001017)
286. feature "COX14" (0.001016)
287. feature "PFN1" (0.001014)
288. feature "ROMO1" (0.001012)
289. feature "ZNF629" (0.001007)
290. feature "MT-CO2" (0.001007)
291. feature "TASOR2" (0.001006)
292. feature "ESRP2" (0.001005)
293. feature "NEAT1" (0.001004)
294. feature "RPS6KA6" (0.001001)
295. feature "GOLGA5" (0.001000)
296. feature "MYL12B" (0.001000)
297. feature "RPSAP48" (0.000999)
298. feature "FOXP4" (0.000997)
299. feature "ODF2L" (0.000991)
300. feature "NPM1P40" (0.000986)
301. feature "FOSL1" (0.000986)
302. feature "SOX4" (0.000978)
303. feature "LMNB2" (0.000977)
304. feature "PGAM1" (0.000974)
305. feature "KIF3B" (0.000973)
306. feature "CETN3" (0.000972)
307. feature "SUMO3" (0.000970)
308. feature "GTF2F1" (0.000968)
309. feature "C6orf62" (0.000957)
310. feature "PIP4K2A" (0.000956)
311. feature "MT-CO1" (0.000956)
312. feature "CENPE" (0.000955)
313. feature "SNRPD2" (0.000951)
314. feature "AHNAK2" (0.000950)
315. feature "RHOD" (0.000948)
316. feature "WSB2" (0.000947)
317. feature "MT-ND2" (0.000946)
318. feature "MRPL14" (0.000946)
319. feature "TMPRSS4" (0.000946)
320. feature "NDUFB10" (0.000945)
321. feature "AP1G1" (0.000941)
322. feature "THRB" (0.000938)
323. feature "MAVS" (0.000937)
324. feature "DNAJA1" (0.000937)
325. feature "CHP1" (0.000935)
326. feature "UBQLN4" (0.000933)
327. feature "H2AC12" (0.000929)
328. feature "PACS2" (0.000928)
329. feature "RPL15" (0.000927)
330. feature "UCA1" (0.000926)
331. feature "ANKRD52" (0.000924)
332. feature "ACTG1" (0.000923)
333. feature "CANT1" (0.000922)
334. feature "SMARCD2" (0.000919)
335. feature "MT-TQ" (0.000914)
336. feature "SLC25A37" (0.000913)
337. feature "IGFBP4" (0.000910)
338. feature "H2AC20" (0.000910)
339. feature "DDX54" (0.000908)
340. feature "CCDC34" (0.000908)
341. feature "MCM5" (0.000907)
342. feature "CACNA1A" (0.000907)
343. feature "PHGDH" (0.000904)
344. feature "NCL" (0.000901)
345. feature "DNAJA2" (0.000901)
346. feature "KRT7" (0.000900)
347. feature "CITED2" (0.000897)
348. feature "KRT5" (0.000896)
349. feature "MT1X" (0.000895)
350. feature "MARCKS" (0.000895)
351. feature "SRC" (0.000894)
352. feature "CALR" (0.000893)
353. feature "ATP5MG" (0.000893)
354. feature "CDK5RAP2" (0.000891)
355. feature "CSRP1" (0.000891)
356. feature "RPL13" (0.000889)
357. feature "PPP1R14B" (0.000888)
358. feature "TSR1" (0.000887)
359. feature "RPS15A" (0.000883)
360. feature "MAP4K4" (0.000883)
361. feature "RRM1" (0.000880)
362. feature "SECISBP2" (0.000879)
363. feature "AMOTL1" (0.000879)
364. feature "PLBD2" (0.000879)
365. feature "CTNNA1-AS1" (0.000879)
366. feature "TFF3" (0.000874)
367. feature "INPP4B" (0.000873)
368. feature "PRPF40A" (0.000869)
369. feature "RPL23" (0.000869)
370. feature "SYTL2" (0.000867)
371. feature "PSIP1" (0.000864)
372. feature "CAMSAP2" (0.000862)
373. feature "C16orf91" (0.000861)
374. feature "TK1" (0.000860)
375. feature "RNF20" (0.000855)
376. feature "H2BC9" (0.000853)
377. feature "TRIM41" (0.000851)
378. feature "SSNA1" (0.000849)
379. feature "S100A6" (0.000849)
380. feature "PROSER1" (0.000849)
381. feature "LPP" (0.000843)
382. feature "EPN3" (0.000840)
383. feature "TAOK1" (0.000838)
384. feature "RAB11B" (0.000837)
385. feature "KRT10" (0.000837)
386. feature "PFKP" (0.000835)
387. feature "RNF122" (0.000835)
388. feature "MTA2" (0.000831)
389. feature "PKIB" (0.000830)
390. feature "TAF13" (0.000830)
391. feature "HMGN5" (0.000829)
392. feature "RALGPS1" (0.000829)
393. feature "NETO2" (0.000828)
394. feature "CALM2" (0.000826)
395. feature "RPS19BP1" (0.000826)
396. feature "GTF3A" (0.000824)
397. feature "HIF1A" (0.000823)
398. feature "CDV3" (0.000823)
399. feature "UQCR11" (0.000823)
400. feature "RPS28" (0.000823)
401. feature "RPS3" (0.000822)
402. feature "METAP2" (0.000818)
403. feature "RPL12" (0.000817)
404. feature "RPLP1" (0.000817)
405. feature "RAB13" (0.000816)
406. feature "SCP2" (0.000816)
407. feature "FASTK" (0.000815)
408. feature "LAD1" (0.000811)
409. feature "MBD2" (0.000810)
410. feature "GBP1P1" (0.000809)
411. feature "ACKR3" (0.000804)
412. feature "TIAL1" (0.000804)
413. feature "FGF12" (0.000803)
414. feature "TPP1" (0.000802)
415. feature "PI4KB" (0.000800)
416. feature "HCFC1R1" (0.000800)
417. feature "DONSON" (0.000798)
418. feature "EPB41L1" (0.000798)
419. feature "ZC3H15" (0.000796)
420. feature "TMA7" (0.000796)
421. feature "MARK3" (0.000792)
422. feature "DLD" (0.000789)
423. feature "RHOBTB3" (0.000786)
424. feature "KEAP1" (0.000786)
425. feature "FTL" (0.000785)
426. feature "LAMB3" (0.000783)
427. feature "ARID4B" (0.000781)
428. feature "SLC9A3R1" (0.000780)
429. feature "ATP5F1E" (0.000778)
430. feature "DPM3" (0.000776)
431. feature "RAD23A" (0.000776)
432. feature "MGLL" (0.000773)
433. feature "SOS2" (0.000771)
434. feature "NCALD" (0.000770)
435. feature "LEO1" (0.000769)
436. feature "ASB8" (0.000765)
437. feature "PPDPF" (0.000763)
438. feature "ARSA" (0.000758)
439. feature "PM20D2" (0.000758)
440. feature "ERBIN" (0.000758)
441. feature "CMPK1" (0.000757)
442. feature "MLXIP" (0.000755)
443. feature "PML" (0.000752)
444. feature "PTMS" (0.000750)
445. feature "CLTB" (0.000748)
446. feature "UNC13D" (0.000747)
447. feature "S100A11" (0.000744)
448. feature "PPP2R1A" (0.000743)
449. feature "PDCD4" (0.000742)
450. feature "PPP4R2" (0.000742)
451. feature "NDUFB1" (0.000741)
452. feature "WNK1" (0.000741)
453. feature "REST" (0.000740)
454. feature "STRN4" (0.000739)
455. feature "CSTA" (0.000737)
456. feature "SERF2" (0.000735)
457. feature "PREB" (0.000732)
458. feature "PRPF19" (0.000732)
459. feature "ATAD2" (0.000730)
460. feature "FAM107B" (0.000728)
461. feature "PLEKHH3" (0.000728)
462. feature "RPS8" (0.000726)
463. feature "NBN" (0.000724)
464. feature "TPD52L1" (0.000723)
465. feature "RPS27" (0.000721)
466. feature "PPP2R5D" (0.000719)
467. feature "RPSA" (0.000713)
468. feature "ZCRB1" (0.000711)
469. feature "XRRA1" (0.000710)
470. feature "C4orf48" (0.000709)
471. feature "DHX37" (0.000708)
472. feature "C1orf21" (0.000707)
473. feature "PRRG3" (0.000707)
474. feature "RPL7A" (0.000706)
475. feature "SDC1" (0.000705)
476. feature "FSBP" (0.000705)
477. feature "TCEAL3" (0.000704)
478. feature "KIF5B" (0.000704)
479. feature "RB1CC1" (0.000697)
480. feature "EEF2" (0.000696)
481. feature "UBC" (0.000695)
482. feature "VTI1B" (0.000693)
483. feature "EFHD1" (0.000692)
484. feature "HCFC1" (0.000692)
485. feature "SLPI" (0.000691)
486. feature "HSPA4" (0.000690)
487. feature "PARD6B" (0.000689)
488. feature "VMP1" (0.000689)
489. feature "ANXA1" (0.000688)
490. feature "MT-ATP8" (0.000687)
491. feature "SFN" (0.000687)
492. feature "COTL1" (0.000686)
493. feature "ZNF696" (0.000686)
494. feature "RPS2" (0.000683)
495. feature "ZHX1" (0.000683)
496. feature "COX7C" (0.000680)
497. feature "HSBP1" (0.000679)
498. feature "YWHAZ" (0.000679)
499. feature "ZBTB37" (0.000676)
500. feature "GSTP1" (0.000675)
501. feature "CDIPT" (0.000674)
502. feature "ILF3" (0.000674)
503. feature "H2AC11" (0.000674)
504. feature "RNF168" (0.000672)
505. feature "POLR2J" (0.000671)
506. feature "RPL35" (0.000670)
507. feature "DSCAM-AS1" (0.000667)
508. feature "RPL27A" (0.000667)
509. feature "KRT13" (0.000663)
510. feature "PHF3" (0.000663)
511. feature "PGLS" (0.000661)
512. feature "LSM4" (0.000661)
513. feature "ING2" (0.000659)
514. feature "RTF1" (0.000659)
515. feature "COX7A2" (0.000659)
516. feature "C2orf49" (0.000658)
517. feature "ATP5MK" (0.000658)
518. feature "RPS15" (0.000657)
519. feature "BASP1" (0.000657)
520. feature "RBBP6" (0.000656)
521. feature "CHTOP" (0.000655)
522. feature "MT2A" (0.000652)
523. feature "SMDT1" (0.000652)
524. feature "CTNND1" (0.000651)
525. feature "SLTM" (0.000651)
526. feature "POLE4" (0.000649)
527. feature "COPS9" (0.000648)
528. feature "H4C3" (0.000647)
529. feature "SMC6" (0.000647)
530. feature "OAZ1" (0.000645)
531. feature "POLDIP2" (0.000645)
532. feature "METTL26" (0.000644)
533. feature "BTBD9" (0.000643)
534. feature "BOD1L1" (0.000643)
535. feature "TTLL12" (0.000643)
536. feature "FOXP1" (0.000642)
537. feature "ZNF217" (0.000638)
538. feature "MRPS6" (0.000638)
539. feature "RFC1" (0.000637)
540. feature "NUP62" (0.000635)
541. feature "CEP55" (0.000635)
542. feature "C5orf63" (0.000634)
543. feature "FMN1" (0.000634)
544. feature "HSP90B1" (0.000634)
545. feature "TMEM258" (0.000633)
546. feature "TOP2A" (0.000631)
547. feature "THEM4" (0.000630)
548. feature "SIGMAR1" (0.000629)
549. feature "ZNF302" (0.000629)
550. feature "PSMA7" (0.000628)
551. feature "TSPO" (0.000628)
552. feature "RAC1" (0.000628)
553. feature "FOSL2" (0.000625)
554. feature "EMP2" (0.000622)
555. feature "DIO2" (0.000621)
556. feature "RPL8" (0.000621)
557. feature "PRRC2C" (0.000620)
558. feature "RPS6KB1" (0.000618)
559. feature "AHNAK" (0.000618)
560. feature "HSP90AB1" (0.000618)
561. feature "GATA3" (0.000617)
562. feature "PC" (0.000614)
563. feature "MT-ND5" (0.000614)
564. feature "CAST" (0.000611)
565. feature "LINC01304" (0.000607)
566. feature "GMFB" (0.000606)
567. feature "TPM4" (0.000605)
568. feature "NEDD4L" (0.000605)
569. feature "RGPD4-AS1" (0.000604)
570. feature "MGP" (0.000603)
571. feature "ANKRD11" (0.000603)
572. feature "PLEC" (0.000600)
573. feature "LUZP1" (0.000600)
574. feature "UQCC2" (0.000598)
575. feature "PCF11" (0.000596)
576. feature "KCNQ1OT1" (0.000595)
577. feature "TMSB4X" (0.000595)
578. feature "TRIP12" (0.000594)
579. feature "ZNF148" (0.000593)
580. feature "NCOA3" (0.000592)
581. feature "CAV1" (0.000592)
582. feature "ELP3" (0.000592)
583. feature "ATRX" (0.000592)
584. feature "EIF4EBP2" (0.000591)
585. feature "TPM3" (0.000591)
586. feature "H2BC4" (0.000591)
587. feature "PKP1" (0.000590)
588. feature "DSP" (0.000589)
589. feature "HNRNPD" (0.000588)
590. feature "RBM25" (0.000586)
591. feature "YBX1" (0.000585)
592. feature "DGKZ" (0.000581)
593. feature "PFKFB3" (0.000580)
594. feature "KRT14" (0.000579)
595. feature "NIN" (0.000578)
596. feature "SCYL3" (0.000578)
597. feature "PCSK5" (0.000578)
598. feature "RBM23" (0.000577)
599. feature "TRIM44" (0.000576)
600. feature "RPL37A" (0.000576)
601. feature "GOLGA4" (0.000576)
602. feature "STMN1" (0.000574)
603. feature "NCOR1" (0.000570)
604. feature "RHOV" (0.000570)
605. feature "RPL36" (0.000570)
606. feature "ZNF609" (0.000569)
607. feature "PKP4" (0.000569)
608. feature "H1-4" (0.000565)
609. feature "RPL35A" (0.000564)
610. feature "TRIM37" (0.000563)
611. feature "SAMD4B" (0.000562)
612. feature "ERO1A" (0.000562)
613. feature "KLHL7" (0.000560)
614. feature "R3HDM2" (0.000558)
615. feature "RBFOX2" (0.000558)
616. feature "TRAK1" (0.000557)
617. feature "ZC3H7A" (0.000555)
618. feature "LARP4B" (0.000554)
619. feature "UQCC3" (0.000553)
620. feature "UIMC1" (0.000552)
621. feature "NEDD8" (0.000551)
622. feature "HNRNPM" (0.000551)
623. feature "DBNDD1" (0.000550)
624. feature "MARCHF6" (0.000550)
625. feature "KRT18" (0.000550)
626. feature "PDIA3" (0.000547)
627. feature "ELOB" (0.000545)
628. feature "APLP2" (0.000544)
629. feature "PAK2" (0.000542)
630. feature "TEDC2-AS1" (0.000542)
631. feature "TMEM141" (0.000541)
632. feature "DCTN1" (0.000541)
633. feature "MYH14" (0.000538)
634. feature "RPS29" (0.000537)
635. feature "AP2A1" (0.000534)
636. feature "CENPF" (0.000534)
637. feature "NRBP1" (0.000534)
638. feature "BAZ1B" (0.000533)
639. feature "ZBTB20" (0.000533)
640. feature "NECAB1" (0.000532)
641. feature "SVIL-AS1" (0.000531)
642. feature "MYO5C" (0.000530)
643. feature "UBE2E3" (0.000529)
644. feature "POLR3A" (0.000529)
645. feature "CAPZA1" (0.000527)
646. feature "RPS16" (0.000527)
647. feature "TBC1D9" (0.000526)
648. feature "GPI" (0.000526)
649. feature "CLIC1" (0.000525)
650. feature "FOXK1" (0.000521)
651. feature "RAB5A" (0.000516)
652. feature "LBH" (0.000516)
653. feature "RPL29" (0.000515)
654. feature "RPLP2" (0.000515)
655. feature "CSDE1" (0.000513)
656. feature "DKC1" (0.000512)
657. feature "MYH9" (0.000508)
658. feature "CAMSAP1" (0.000507)
659. feature "BDP1" (0.000507)
660. feature "DPH1" (0.000507)
661. feature "WDR18" (0.000505)
662. feature "CAPNS1" (0.000504)
663. feature "ITPRID2" (0.000503)
664. feature "MT-TY" (0.000503)
665. feature "SMIM10L1" (0.000502)
666. feature "UQCRQ" (0.000501)
667. feature "AAMP" (0.000500)
668. feature "PSMD14" (0.000500)
669. feature "NDUFB2" (0.000499)
670. feature "HSPA5" (0.000498)
671. feature "PRR34-AS1" (0.000497)
672. feature "ILRUN" (0.000496)
673. feature "SOX15" (0.000496)
674. feature "FAM104A" (0.000491)
675. feature "AGBL5" (0.000490)
676. feature "NAGK" (0.000485)
677. feature "TWNK" (0.000484)
678. feature "CDC42BPB" (0.000484)
679. feature "EEF1A1" (0.000483)
680. feature "PCGF3" (0.000482)
681. feature "ITSN2" (0.000481)
682. feature "IGFBP5" (0.000481)
683. feature "SNHG6" (0.000480)
684. feature "RPL28" (0.000480)
685. feature "ZNF580" (0.000480)
686. feature "BCAS3" (0.000480)
687. feature "SMARCB1" (0.000479)
688. feature "MKI67" (0.000479)
689. feature "TMEM160" (0.000479)
690. feature "DEK" (0.000478)
691. feature "BRD4" (0.000477)
692. feature "BPTF" (0.000476)
693. feature "MORN2" (0.000475)
694. feature "PTBP3" (0.000474)
695. feature "HNRNPA2B1" (0.000473)
696. feature "SMIM27" (0.000472)
697. feature "MTMR12" (0.000470)
698. feature "FUBP3" (0.000467)
699. feature "NUDT1" (0.000464)
700. feature "CYB5R3" (0.000464)
701. feature "S100A2" (0.000463)
702. feature "ATP1A1" (0.000462)
703. feature "SALL4" (0.000461)
704. feature "PDAP1" (0.000461)
705. feature "PRMT2" (0.000459)
706. feature "PAPOLA" (0.000459)
707. feature "EIF3A" (0.000458)
708. feature "YWHAB" (0.000458)
709. feature "AP2S1" (0.000457)
710. feature "RPS12" (0.000456)
711. feature "H1-1" (0.000455)
712. feature "INF2" (0.000455)
713. feature "H1-5" (0.000451)
714. feature "RANGAP1" (0.000450)
715. feature "ROCK2" (0.000450)
716. feature "MAPKAPK2" (0.000450)
717. feature "ZBTB45" (0.000449)
718. feature "ATP5ME" (0.000446)
719. feature "TBC1D20" (0.000446)
720. feature "HTATSF1" (0.000445)
721. feature "DNMT1" (0.000445)
722. feature "VMAC" (0.000445)
723. feature "POLR2L" (0.000443)
724. feature "LARP7" (0.000442)
725. feature "HDGF" (0.000442)
726. feature "CTXN1" (0.000442)
727. feature "H1-0" (0.000441)
728. feature "PALLD" (0.000441)
729. feature "SLC25A48" (0.000441)
730. feature "EZR" (0.000440)
731. feature "ARHGEF12" (0.000437)
732. feature "OIP5-AS1" (0.000437)
733. feature "OPTN" (0.000436)
734. feature "EIF4G2" (0.000436)
735. feature "H4C5" (0.000435)
736. feature "ANP32E" (0.000435)
737. feature "HSPB1" (0.000434)
738. feature "LMX1B" (0.000433)
739. feature "HNRNPU" (0.000433)
740. feature "RRP15" (0.000432)
741. feature "SOS1" (0.000432)
742. feature "ATOX1" (0.000432)
743. feature "PHLDB2" (0.000432)
744. feature "TFRC" (0.000431)
745. feature "CAVIN1" (0.000430)
746. feature "PDLIM1" (0.000430)
747. feature "GNL3L" (0.000430)
748. feature "CMIP" (0.000428)
749. feature "CNNM2" (0.000427)
750. feature "ARID5B" (0.000427)
751. feature "JUP" (0.000426)
752. feature "STC2" (0.000425)
753. feature "TARS1" (0.000425)
754. feature "USP32" (0.000423)
755. feature "H19" (0.000423)
756. feature "H1-3" (0.000422)
757. feature "PABPC1" (0.000422)
758. feature "RPL11" (0.000421)
759. feature "C8orf33" (0.000421)
760. feature "EFCAB14" (0.000420)
761. feature "RPL37" (0.000419)
762. feature "CNBP" (0.000418)
763. feature "ZNF2" (0.000417)
764. feature "BROX" (0.000417)
765. feature "ANP32B" (0.000416)
766. feature "NHS" (0.000415)
767. feature "TNRC6A" (0.000414)
768. feature "CBX3" (0.000414)
769. feature "STX2" (0.000413)
770. feature "EHBP1L1" (0.000412)
771. feature "MYO9A" (0.000409)
772. feature "IRAK1" (0.000406)
773. feature "GUK1" (0.000406)
774. feature "PAN3" (0.000405)
775. feature "GPX4" (0.000405)
776. feature "STK16" (0.000404)
777. feature "PTP4A2" (0.000403)
778. feature "CAVIN3" (0.000401)
779. feature "ARHGAP1" (0.000400)
780. feature "KIF14" (0.000399)
781. feature "MSR1" (0.000398)
782. feature "INCENP" (0.000398)
783. feature "NDUFA2" (0.000397)
784. feature "KLF6" (0.000395)
785. feature "SLC2A1" (0.000394)
786. feature "H1-2" (0.000394)
787. feature "NEMP1" (0.000394)
788. feature "CLIP1" (0.000393)
789. feature "SPATS2L" (0.000393)
790. feature "CAMK2N1" (0.000393)
791. feature "TAF15" (0.000393)
792. feature "ID1" (0.000392)
793. feature "RPL12P38" (0.000391)
794. feature "PPIG" (0.000391)
795. feature "MRPS34" (0.000390)
796. feature "QSOX1" (0.000390)
797. feature "RPL27" (0.000388)
798. feature "SUN2" (0.000388)
799. feature "SRSF1" (0.000386)
800. feature "YES1" (0.000385)
801. feature "ARPP19" (0.000384)
802. feature "DDX21" (0.000384)
803. feature "CCDC26" (0.000384)
804. feature "MLYCD" (0.000383)
805. feature "EEA1" (0.000382)
806. feature "HMGB2" (0.000379)
807. feature "DBF4" (0.000379)
808. feature "NMD3" (0.000377)
809. feature "SEPTIN9" (0.000377)
810. feature "ZC3H13" (0.000376)
811. feature "AREG" (0.000375)
812. feature "RAB5C" (0.000372)
813. feature "DTYMK" (0.000371)
814. feature "BRIP1" (0.000370)
815. feature "SIVA1" (0.000368)
816. feature "MRPL33" (0.000366)
817. feature "GADD45A" (0.000366)
818. feature "PPL" (0.000364)
819. feature "YBX3" (0.000364)
820. feature "PDIA6" (0.000363)
821. feature "TUBD1" (0.000361)
822. feature "MAN1A2" (0.000360)
823. feature "JUN" (0.000359)
824. feature "FAM177A1" (0.000357)
825. feature "S100A9" (0.000356)
826. feature "KCNJ2" (0.000354)
827. feature "SKI" (0.000354)
828. feature "PAIP2" (0.000353)
829. feature "ETAA1" (0.000352)
830. feature "PLEKHA4" (0.000351)
831. feature "GDE1" (0.000349)
832. feature "HEPACAM" (0.000349)
833. feature "ZNF512B" (0.000349)
834. feature "EFNA5" (0.000348)
835. feature "TRMT12" (0.000347)
836. feature "SINHCAFP3" (0.000347)
837. feature "ZMIZ1" (0.000344)
838. feature "RAP2B" (0.000344)
839. feature "KRT8" (0.000344)
840. feature "CAPZB" (0.000344)
841. feature "TPX2" (0.000344)
842. feature "ABCA1" (0.000343)
843. feature "EIF4G3" (0.000343)
844. feature "NOTCH2NLC" (0.000342)
845. feature "TNFSF13B" (0.000341)
846. feature "MAP7" (0.000339)
847. feature "NTN4" (0.000338)
848. feature "TCHP" (0.000338)
849. feature "RPL18" (0.000337)
850. feature "DLG5" (0.000337)
851. feature "DAXX" (0.000334)
852. feature "RFFL" (0.000332)
853. feature "MKNK1" (0.000331)
854. feature "EML6" (0.000331)
855. feature "IFI27L2" (0.000329)
856. feature "SYNE2" (0.000329)
857. feature "DHX38" (0.000327)
858. feature "TFE3" (0.000326)
859. feature "RNPS1" (0.000325)
860. feature "RNF34" (0.000325)
861. feature "ABCC4" (0.000324)
862. feature "AP1M1" (0.000324)
863. feature "CPEB4" (0.000323)
864. feature "BAP1" (0.000323)
865. feature "ZNF180" (0.000322)
866. feature "ARHGAP11A" (0.000321)
867. feature "HMGA1" (0.000319)
868. feature "MIR4458HG" (0.000319)
869. feature "ZBTB26" (0.000317)
870. feature "MCM4" (0.000316)
871. feature "FABP5" (0.000315)
872. feature "SLC25A23" (0.000313)
873. feature "ARF3" (0.000311)
874. feature "DNAJC2" (0.000311)
875. feature "GRB10" (0.000310)
876. feature "CSTB" (0.000310)
877. feature "ZC3H18" (0.000310)
878. feature "FBXL16" (0.000310)
879. feature "ROGDI" (0.000309)
880. feature "KLHL8" (0.000309)
881. feature "MORC3" (0.000309)
882. feature "PCBP1" (0.000309)
883. feature "YTHDF1" (0.000308)
884. feature "UBXN2B" (0.000308)
885. feature "TAGLN2" (0.000304)
886. feature "RP9" (0.000302)
887. feature "BTN3A2" (0.000302)
888. feature "CMBL" (0.000302)
889. feature "ZMYND8" (0.000301)
890. feature "RBX1" (0.000301)
891. feature "RPS27L" (0.000300)
892. feature "CS" (0.000298)
893. feature "MT-TE" (0.000298)
894. feature "DAAM1" (0.000297)
895. feature "CDC37" (0.000297)
896. feature "SETD2" (0.000295)
897. feature "CMSS1" (0.000294)
898. feature "HSPBP1" (0.000294)
899. feature "NEUROD2" (0.000292)
900. feature "GSS" (0.000291)
901. feature "ADGRV1" (0.000291)
902. feature "LYPLA2" (0.000291)
903. feature "SART1" (0.000288)
904. feature "TIMM8B" (0.000287)
905. feature "SEMA3D" (0.000282)
906. feature "SHOX" (0.000282)
907. feature "APOOL" (0.000280)
908. feature "EXOC7" (0.000279)
909. feature "FGFR1OP2" (0.000279)
910. feature "RAB40C" (0.000278)
911. feature "NAA10" (0.000277)
912. feature "DNMT3A" (0.000275)
913. feature "CLSPN" (0.000274)
914. feature "ZZEF1" (0.000274)
915. feature "KPNA2" (0.000273)
916. feature "CACNB3" (0.000272)
917. feature "MDC1" (0.000271)
918. feature "MINDY2" (0.000271)
919. feature "PAQR7" (0.000270)
920. feature "METRN" (0.000270)
921. feature "RAD23B" (0.000268)
922. feature "HNRNPUL1" (0.000267)
923. feature "SGO2" (0.000266)
924. feature "RAB1B" (0.000265)
925. feature "PRR5L" (0.000264)
926. feature "CD151" (0.000262)
927. feature "CCNG2" (0.000261)
928. feature "MACC1" (0.000261)
929. feature "C9orf78" (0.000260)
930. feature "SMC4" (0.000260)
931. feature "KDM1A" (0.000259)
932. feature "GPATCH4" (0.000258)
933. feature "TAF3" (0.000257)
934. feature "DUS4L" (0.000256)
935. feature "CCP110" (0.000256)
936. feature "MAFF" (0.000255)
937. feature "SNHG9" (0.000255)
938. feature "GALM" (0.000253)
939. feature "DOHH" (0.000252)
940. feature "MZT2B" (0.000252)
941. feature "FAAP100" (0.000252)
942. feature "FBXW5" (0.000251)
943. feature "SNHG18" (0.000251)
944. feature "MEX3D" (0.000250)
945. feature "TBCA" (0.000250)
946. feature "VRK3" (0.000250)
947. feature "SMAD3" (0.000249)
948. feature "TUBB6" (0.000248)
949. feature "TMEM115" (0.000248)
950. feature "PMEPA1" (0.000247)
951. feature "ELOA" (0.000246)
952. feature "ARHGAP23" (0.000246)
953. feature "HRAS" (0.000245)
954. feature "GCC2" (0.000245)
955. feature "RAB17" (0.000244)
956. feature "NCOR2" (0.000242)
957. feature "NCOA2" (0.000240)
958. feature "NCBP3" (0.000240)
959. feature "HNRNPAB" (0.000240)
960. feature "SEPTIN8" (0.000239)
961. feature "PHACTR2" (0.000238)
962. feature "CKS2" (0.000237)
963. feature "EIF5B" (0.000233)
964. feature "BIK" (0.000231)
965. feature "WDR24" (0.000230)
966. feature "CDKN3" (0.000230)
967. feature "CACNG4" (0.000226)
968. feature "MOB3A" (0.000226)
969. feature "FAM120A" (0.000226)
970. feature "TMEM52B" (0.000224)
971. feature "FAM200B" (0.000222)
972. feature "SPDL1" (0.000221)
973. feature "VPS4A" (0.000220)
974. feature "PPM1G" (0.000220)
975. feature "HMBS" (0.000220)
976. feature "MTUS1" (0.000219)
977. feature "APPL1" (0.000217)
978. feature "TCEAL4" (0.000216)
979. feature "RAB4A" (0.000216)
980. feature "MYO10" (0.000216)
981. feature "S100P" (0.000215)
982. feature "RIPK2" (0.000215)
983. feature "SNRPA" (0.000214)
984. feature "CDAN1" (0.000214)
985. feature "ROCK1" (0.000211)
986. feature "SPATA33" (0.000210)
987. feature "NSD1" (0.000208)
988. feature "HCG11" (0.000207)
989. feature "SPRYD3" (0.000207)
990. feature "CNN3" (0.000206)
991. feature "NIPSNAP3A" (0.000206)
992. feature "MINK1" (0.000205)
993. feature "EID1" (0.000205)
994. feature "GNPAT" (0.000204)
995. feature "BACE2" (0.000203)
996. feature "PATJ" (0.000202)
997. feature "SDHAF3" (0.000202)
998. feature "CASP8AP2" (0.000201)
999. feature "H2AX" (0.000200)
1000. feature "TPH1" (0.000200)
1001. feature "XBP1" (0.000199)
1002. feature "THOC7" (0.000198)
1003. feature "CDK2AP1" (0.000197)
1004. feature "POGK" (0.000195)
1005. feature "ATP2A2" (0.000194)
1006. feature "SNX27" (0.000193)
1007. feature "GNL1" (0.000192)
1008. feature "SFXN3" (0.000188)
1009. feature "CROCC" (0.000188)
1010. feature "HBEGF" (0.000186)
1011. feature "POLK" (0.000185)
1012. feature "PRXL2C" (0.000182)
1013. feature "GABPA" (0.000180)
1014. feature "FAM83A" (0.000180)
1015. feature "VDR" (0.000177)
1016. feature "FEM1A" (0.000174)
1017. feature "SENP3" (0.000173)
1018. feature "AKT1S1" (0.000172)
1019. feature "MZT2A" (0.000169)
1020. feature "YTHDF3" (0.000169)
1021. feature "CMAS" (0.000167)
1022. feature "CAPN1" (0.000166)
1023. feature "YJU2" (0.000166)
1024. feature "SMIM15" (0.000166)
1025. feature "ZNF446" (0.000164)
1026. feature "LCLAT1" (0.000163)
1027. feature "NONO" (0.000163)
1028. feature "SLC25A24" (0.000158)
1029. feature "S100A16" (0.000157)
1030. feature "ZZZ3" (0.000156)
1031. feature "SCLY" (0.000155)
1032. feature "ATP5MC2" (0.000155)
1033. feature "FBXL17" (0.000154)
1034. feature "SMARCC1" (0.000154)
1035. feature "CALB1" (0.000152)
1036. feature "TSSC4" (0.000149)
1037. feature "CCDC167" (0.000146)
1038. feature "HPCAL1" (0.000146)
1039. feature "HSPH1" (0.000141)
1040. feature "GIT1" (0.000141)
1041. feature "TRPV4" (0.000140)
1042. feature "HMG20B" (0.000140)
1043. feature "FAM199X" (0.000138)
1044. feature "SENP6" (0.000137)
1045. feature "BRF1" (0.000136)
1046. feature "KLF9" (0.000136)
1047. feature "DVL3" (0.000133)
1048. feature "CCNY" (0.000131)
1049. feature "EPS8L1" (0.000128)
1050. feature "TAOK3" (0.000127)
1051. feature "MYADM" (0.000126)
1052. feature "SRI" (0.000125)
1053. feature "H2AC16" (0.000125)
1054. feature "GTF3C2" (0.000123)
1055. feature "NPFFR1" (0.000122)
1056. feature "TOP2B" (0.000122)
1057. feature "SLC19A2" (0.000121)
1058. feature "EPAS1" (0.000120)
1059. feature "SBF1" (0.000119)
1060. feature "SETD3" (0.000118)
1061. feature "CTNS" (0.000115)
1062. feature "VAV2" (0.000113)
1063. feature "KIF26A" (0.000112)
1064. feature "LINC01234" (0.000111)
1065. feature "NEFL" (0.000109)
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8420. feature "CCDC71L" (0.000000)
8421. feature "DALRD3" (0.000000)
8422. feature "KIFC2" (0.000000)
8423. feature "HLA-V" (0.000000)
8424. feature "MAP3K10" (0.000000)
8425. feature "YBX1P6" (0.000000)
8426. feature "RAB9A" (0.000000)
8427. feature "C19orf44" (0.000000)
8428. feature "MPPE1" (0.000000)
8429. feature "TINF2" (0.000000)
8430. feature "CRLF1" (0.000000)
8431. feature "FMC1" (0.000000)
8432. feature "PLCXD2" (0.000000)
8433. feature "PITPNM1" (0.000000)
8434. feature "SLC25A28" (0.000000)
8435. feature "THAP2" (0.000000)
8436. feature "BBS4" (0.000000)
8437. feature "RNF170" (0.000000)
8438. feature "STARD4" (0.000000)
8439. feature "HNF4A" (0.000000)
8440. feature "OSER1-DT" (0.000000)
8441. feature "ITM2C" (0.000000)
8442. feature "MMAA" (0.000000)
8443. feature "TCAIM" (0.000000)
8444. feature "SMIM4" (0.000000)
8445. feature "SCRIB" (0.000000)
8446. feature "THNSL1" (0.000000)
8447. feature "DANT2" (0.000000)
8448. feature "FAM86C2P" (0.000000)
8449. feature "SNX11" (0.000000)
8450. feature "SREK1IP1P1" (0.000000)
8451. feature "FRG1JP" (0.000000)
8452. feature "RHOB" (0.000000)
8453. feature "CCDC62" (0.000000)
8454. feature "MZT1" (0.000000)
8455. feature "MUC4" (0.000000)
8456. feature "ARMH3" (0.000000)
8457. feature "LMF2" (0.000000)
8458. feature "ZC3H10" (0.000000)
8459. feature "TMEM241" (0.000000)
8460. feature "ZNF317" (0.000000)
8461. feature "BIN1" (0.000000)
8462. feature "STIM2-AS1" (0.000000)
8463. feature "LINC02541" (0.000000)
8464. feature "ZFP28" (0.000000)
8465. feature "GORASP1" (0.000000)
8466. feature "GMPR2" (0.000000)
8467. feature "SLC39A14" (0.000000)
8468. feature "MT-TT" (0.000000)
8469. feature "TTC30A" (0.000000)
8470. feature "RPGR" (0.000000)
8471. feature "SNHG19" (0.000000)
8472. feature "LHX6" (0.000000)
8473. feature "LINC01139" (0.000000)
8474. feature "PITX1-AS1" (0.000000)
8475. feature "LINC01666" (0.000000)
8476. feature "ULK1" (0.000000)
8477. feature "CRPPA" (0.000000)
8478. feature "GLB1" (0.000000)
8479. feature "PNPLA3" (0.000000)
8480. feature "NRGN" (0.000000)
8481. feature "OXSM" (0.000000)
8482. feature "KBTBD7" (0.000000)
8483. feature "DNLZ" (0.000000)
8484. feature "NEK3" (0.000000)
8485. feature "ST13P7" (0.000000)
8486. feature "FOXD1" (0.000000)
8487. feature "CFAP410" (0.000000)
8488. feature "OR7E14P" (0.000000)
8489. feature "ADTRP" (0.000000)
8490. feature "TEX9" (0.000000)
8491. feature "UBE2I" (0.000000)
8492. feature "AMZ2P1" (0.000000)
8493. feature "ALG13" (0.000000)
8494. feature "TMEM79" (0.000000)
8495. feature "BAIAP2-DT" (0.000000)
8496. feature "GAREM2" (0.000000)
8497. feature "SORBS3" (0.000000)
8498. feature "ZDHHC18" (0.000000)
8499. feature "GCAT" (0.000000)
8500. feature "ACVR1C" (0.000000)
8501. feature "ZFYVE27" (0.000000)
8502. feature "TRPM2-AS" (0.000000)
8503. feature "NLE1" (0.000000)
8504. feature "CHIC2" (0.000000)
8505. feature "IQANK1" (0.000000)
8506. feature "LINC01133" (0.000000)
8507. feature "BTBD6" (0.000000)
8508. feature "POLR3GL" (0.000000)
8509. feature "MTND2P28" (0.000000)
8510. feature "HAGLROS" (0.000000)
8511. feature "SPATA6L" (0.000000)
8512. feature "BOK" (0.000000)
8513. feature "RPS15AP29" (0.000000)
8514. feature "PRXL2B" (0.000000)
8515. feature "MTCO1P12" (0.000000)
8516. feature "HES4" (0.000000)
8517. feature "ZNF280B" (0.000000)
8518. feature "SDAD1P1" (0.000000)
8519. feature "PPP2R3B" (0.000000)
8520. feature "WAC-AS1" (0.000000)
8521. feature "ENKD1" (0.000000)
8522. feature "ZBTB33" (0.000000)
8523. feature "CDR2" (0.000000)
8524. feature "IRS2" (0.000000)
8525. feature "TNFRSF14" (0.000000)
8526. feature "CRACR2A" (0.000000)
8527. feature "FDXACB1" (0.000000)
8528. feature "LCTL" (0.000000)
8529. feature "IMP3" (0.000000)
8530. feature "MIR137HG" (0.000000)
8531. feature "INTS6-AS1" (0.000000)
8532. feature "OR52A1" (0.000000)
8533. feature "PEX11B" (0.000000)
8534. feature "SMIM30" (0.000000)
8535. feature "TMEM179B" (0.000000)
8536. feature "UPF3AP2" (0.000000)
8537. feature "CNOT6LP1" (0.000000)
8538. feature "SREBF2-AS1" (0.000000)
8539. feature "MYH15" (0.000000)
8540. feature "RASGRP2" (0.000000)
8541. feature "OPRM1" (0.000000)
8542. feature "SHMT1" (0.000000)
8543. feature "INCA1" (0.000000)
8544. feature "INTS9" (0.000000)
8545. feature "ZP3" (0.000000)
8546. feature "C11orf71" (0.000000)
8547. feature "LINC01667" (0.000000)
8548. feature "ZDHHC1" (0.000000)
8549. feature "TPRG1" (0.000000)
8550. feature "VPS16" (0.000000)
8551. feature "C20orf96" (0.000000)
8552. feature "CRB3" (0.000000)
8553. feature "C9orf64" (0.000000)
8554. feature "LINC00320" (0.000000)
8555. feature "DENND2D" (0.000000)
8556. feature "ALDH6A1" (0.000000)
8557. feature "POTEC" (0.000000)
8558. feature "STAG3L5P-PVRIG2P-PILRB" (0.000000)
8559. feature "ZKSCAN2" (0.000000)
8560. feature "PPIL1" (0.000000)
8561. feature "FUOM" (0.000000)
8562. feature "VPS52" (0.000000)
8563. feature "CAPN10" (0.000000)
8564. feature "LINC02899" (0.000000)
8565. feature "RPL24P9" (0.000000)
8566. feature "YPEL3" (0.000000)
8567. feature "LRRC26" (0.000000)
8568. feature "SLC22A5" (0.000000)
8569. feature "RPL5P3" (0.000000)
8570. feature "RNPEPL1" (0.000000)
8571. feature "KMT2E-AS1" (0.000000)
8572. feature "MESP1" (0.000000)
8573. feature "ZNF251" (0.000000)
8574. feature "RHOQP3" (0.000000)
8575. feature "LRCH4" (0.000000)
8576. feature "ABO" (0.000000)
8577. feature "RPL17" (0.000000)
8578. feature "LENG9" (0.000000)
8579. feature "TAF1C" (0.000000)
8580. feature "EXOC3-AS1" (0.000000)
8581. feature "TMEM126A" (0.000000)
8582. feature "FGF22" (0.000000)
8583. feature "LINC01772" (0.000000)
8584. feature "NOP10" (0.000000)
8585. feature "LIMD2" (0.000000)
8586. feature "FIBCD1" (0.000000)
8587. feature "SPHK2" (0.000000)
8588. feature "CUL4B" (0.000000)
8589. feature "CCDC40" (0.000000)
8590. feature "FAM193A" (0.000000)
8591. feature "SAP30-DT" (0.000000)
8592. feature "PAX5" (0.000000)
8593. feature "SMIM1" (0.000000)
8594. feature "OAZ2" (0.000000)
8595. feature "TIGD2" (0.000000)
8596. feature "GAB1" (0.000000)
8597. feature "ABHD14B" (0.000000)
8598. feature "NAPA-AS1" (0.000000)
8599. feature "ARMC5" (0.000000)
8600. feature "ADAM19" (0.000000)
8601. feature "LINC02664" (0.000000)
8602. feature "ENTR1" (0.000000)
8603. feature "FGF8" (0.000000)
8604. feature "HEIH" (0.000000)
8605. feature "PTPN21" (0.000000)
8606. feature "LINC01840" (0.000000)
8607. feature "DET1" (0.000000)
8608. feature "TCL6" (0.000000)
8609. feature "YJEFN3" (0.000000)
8610. feature "ARHGEF33" (0.000000)
8611. feature "PPIAP46" (0.000000)
8612. feature "BRSK2" (0.000000)
8613. feature "FANCF" (0.000000)
8614. feature "ROM1" (0.000000)
8615. feature "SLC9B1" (0.000000)
8616. feature "NRBP2" (0.000000)
8617. feature "MYOSLID" (0.000000)
8618. feature "NFKBIL1" (0.000000)
8619. feature "RPS18P9" (0.000000)
8620. feature "DARS1-AS1" (0.000000)
8621. feature "CSAG3" (0.000000)
8622. feature "MAP3K2-DT" (0.000000)
8623. feature "HOXC6" (0.000000)
8624. feature "MSI1" (0.000000)
8625. feature "TBC1D22A-DT" (0.000000)
8626. feature "CRYZL1" (0.000000)
8627. feature "FAM131C" (0.000000)
8628. feature "FBRSL1" (0.000000)
8629. feature "ANKRD9" (0.000000)
8630. feature "ZNF568" (0.000000)
8631. feature "C5AR1" (0.000000)
8632. feature "ARL15" (0.000000)
8633. feature "NES" (0.000000)
8634. feature "ATP6V0C" (0.000000)
8635. feature "KCTD15" (0.000000)
8636. feature "UBQLN2" (0.000000)
8637. feature "D2HGDH" (0.000000)
8638. feature "MMP25-AS1" (0.000000)
8639. feature "ZNF718" (0.000000)
8640. feature "CCDC51" (0.000000)
8641. feature "FANCE" (0.000000)
8642. feature "SQOR" (0.000000)
8643. feature "HSF2BP" (0.000000)
8644. feature "RRAS" (0.000000)
8645. feature "CYP1B1" (0.000000)
¶
4.6.4) Neural Network
model3 = GP1()
X_train_all_nn, X_val_all_nn, y_train_all_nn, y_val_all_nn = train_test_split(X_train_all.values, y_train_all.values, test_size=0.2, random_state=42)
X_train_all_tensor2 = torch.tensor(X_train_all_nn, dtype=torch.float32)
y_train_all_tensor2 = torch.tensor(y_train_all_nn, dtype=torch.float32).unsqueeze(1)
X_val_all_tensor2 = torch.tensor(X_val_all_nn, dtype=torch.float32)
y_val_all_tensor2 = torch.tensor(y_val_all_nn, dtype=torch.float32).unsqueeze(1) # For binary classification
¶
Training for the model all datasets merged
# We do the same for the main dataset
optimizer = torch.optim.SGD(model3.parameters(), lr=0.01)
criterion = nn.BCEWithLogitsLoss() # applies a sigmoid activation to the model's raw logits and calculates the binary cross-entropy loss
epochs = 1200
best_val_acc = 0
patience = 3
no_improve_epochs = 0
best_model_path3 = 'best_model_final3.pth'
for epoch in range(epochs):
model3.train()
optimizer.zero_grad() # resets the gradients of all model parameters to zero.
train_output = model3(X_train_all_tensor2) # Forward pass with all training data, computes the model's predictions (output) for the entire training dataset
train_loss = criterion(train_output, y_train_all_tensor2) # calculates the difference between predicted outputs and actual labels
train_loss.backward() #computes the gradients of the loss with respect to the model's parameters
# This uses backpropagation to distribute the error back through the network
optimizer.step() #The optimizer applies the selected algorithm (like SGD) to update the weights
# Calculate training accuracy
with torch.no_grad():
train_predictions = torch.round(torch.sigmoid(train_output))
train_correct = (train_predictions == y_train_all_tensor2).sum().item()
train_total = y_train_all_tensor2.size(0)
train_acc = train_correct / train_total
# Validation
model3.eval() # sets the model to evaluation mode, disabling features like dropout and batch normalization to ensure consistency
with torch.no_grad(): #prevents PyTorch from computing gradients during evaluation.
val_output = model3(X_val_all_tensor2) #computes predictions on the validation dataset
val_loss = criterion(val_output, y_val_all_tensor2)
val_predictions = torch.round(torch.sigmoid(val_output)) # converts the logits/predictions into binary predictions (0 or 1)
val_correct = (val_predictions == y_val_all_tensor2).sum().item()
val_total = y_val_all_tensor2.size(0)
val_acc = val_correct / val_total
print(f'Epoch {epoch+1}/{epochs}, '
f'Train Loss: {train_loss.item():.4f}, '
f'Train Accuracy: {train_acc:.4f}, '
f'Val Loss: {val_loss.item():.4f}, '
f'Val Accuracy: {val_acc:.4f}')
Epoch 1/1200, Train Loss: 0.6923, Train Accuracy: 0.5104, Val Loss: 0.6933, Val Accuracy: 0.4848 Epoch 2/1200, Train Loss: 0.6922, Train Accuracy: 0.5093, Val Loss: 0.6933, Val Accuracy: 0.4848 Epoch 3/1200, Train Loss: 0.6922, Train Accuracy: 0.5095, Val Loss: 0.6933, Val Accuracy: 0.4848 Epoch 4/1200, Train Loss: 0.6917, Train Accuracy: 0.5160, Val Loss: 0.6933, Val Accuracy: 0.4848 Epoch 5/1200, Train Loss: 0.6915, Train Accuracy: 0.5154, Val Loss: 0.6933, Val Accuracy: 0.4848 Epoch 6/1200, Train Loss: 0.6915, Train Accuracy: 0.5192, Val Loss: 0.6933, Val Accuracy: 0.4848 Epoch 7/1200, Train Loss: 0.6909, Train Accuracy: 0.5218, Val Loss: 0.6933, Val Accuracy: 0.4848 Epoch 8/1200, Train Loss: 0.6910, Train Accuracy: 0.5235, Val Loss: 0.6933, Val Accuracy: 0.4848 Epoch 9/1200, Train Loss: 0.6907, Train Accuracy: 0.5267, Val Loss: 0.6932, Val Accuracy: 0.4848 Epoch 10/1200, Train Loss: 0.6906, Train Accuracy: 0.5275, Val Loss: 0.6932, Val Accuracy: 0.4848 Epoch 11/1200, Train Loss: 0.6901, Train Accuracy: 0.5342, Val Loss: 0.6932, Val Accuracy: 0.4848 Epoch 12/1200, Train Loss: 0.6900, Train Accuracy: 0.5368, Val Loss: 0.6932, Val Accuracy: 0.4848 Epoch 13/1200, Train Loss: 0.6895, Train Accuracy: 0.5367, Val Loss: 0.6932, Val Accuracy: 0.4848 Epoch 14/1200, Train Loss: 0.6893, Train Accuracy: 0.5392, Val Loss: 0.6932, Val Accuracy: 0.4850 Epoch 15/1200, Train Loss: 0.6898, Train Accuracy: 0.5398, Val Loss: 0.6932, Val Accuracy: 0.4850 Epoch 16/1200, Train Loss: 0.6892, Train Accuracy: 0.5369, Val Loss: 0.6932, Val Accuracy: 0.4850 Epoch 17/1200, Train Loss: 0.6895, Train Accuracy: 0.5367, Val Loss: 0.6932, Val Accuracy: 0.4850 Epoch 18/1200, Train Loss: 0.6886, Train Accuracy: 0.5500, Val Loss: 0.6932, Val Accuracy: 0.4850 Epoch 19/1200, Train Loss: 0.6887, Train Accuracy: 0.5387, Val Loss: 0.6932, Val Accuracy: 0.4850 Epoch 20/1200, Train Loss: 0.6886, Train Accuracy: 0.5443, Val Loss: 0.6932, Val Accuracy: 0.4850 Epoch 21/1200, Train Loss: 0.6884, Train Accuracy: 0.5451, Val Loss: 0.6932, Val Accuracy: 0.4850 Epoch 22/1200, Train Loss: 0.6884, Train Accuracy: 0.5461, Val Loss: 0.6932, Val Accuracy: 0.4852 Epoch 23/1200, Train Loss: 0.6878, Train Accuracy: 0.5526, Val Loss: 0.6932, Val Accuracy: 0.4852 Epoch 24/1200, Train Loss: 0.6875, Train Accuracy: 0.5557, Val Loss: 0.6932, Val Accuracy: 0.4856 Epoch 25/1200, Train Loss: 0.6874, Train Accuracy: 0.5549, Val Loss: 0.6931, Val Accuracy: 0.4854 Epoch 26/1200, Train Loss: 0.6871, Train Accuracy: 0.5569, Val Loss: 0.6931, Val Accuracy: 0.4852 Epoch 27/1200, Train Loss: 0.6871, Train Accuracy: 0.5595, Val Loss: 0.6931, Val Accuracy: 0.4852 Epoch 28/1200, Train Loss: 0.6872, Train Accuracy: 0.5600, Val Loss: 0.6931, Val Accuracy: 0.4852 Epoch 29/1200, Train Loss: 0.6868, Train Accuracy: 0.5627, Val Loss: 0.6931, Val Accuracy: 0.4856 Epoch 30/1200, Train Loss: 0.6860, Train Accuracy: 0.5702, Val Loss: 0.6931, Val Accuracy: 0.4858 Epoch 31/1200, Train Loss: 0.6858, Train Accuracy: 0.5722, Val Loss: 0.6931, Val Accuracy: 0.4870 Epoch 32/1200, Train Loss: 0.6864, Train Accuracy: 0.5662, Val Loss: 0.6930, Val Accuracy: 0.4870 Epoch 33/1200, Train Loss: 0.6856, Train Accuracy: 0.5682, Val Loss: 0.6930, Val Accuracy: 0.4895 Epoch 34/1200, Train Loss: 0.6856, Train Accuracy: 0.5721, Val Loss: 0.6930, Val Accuracy: 0.4911 Epoch 35/1200, Train Loss: 0.6851, Train Accuracy: 0.5777, Val Loss: 0.6930, Val Accuracy: 0.4934 Epoch 36/1200, Train Loss: 0.6848, Train Accuracy: 0.5781, Val Loss: 0.6930, Val Accuracy: 0.4961 Epoch 37/1200, Train Loss: 0.6848, Train Accuracy: 0.5787, Val Loss: 0.6929, Val Accuracy: 0.5002 Epoch 38/1200, Train Loss: 0.6845, Train Accuracy: 0.5758, Val Loss: 0.6929, Val Accuracy: 0.5047 Epoch 39/1200, Train Loss: 0.6847, Train Accuracy: 0.5819, Val Loss: 0.6929, Val Accuracy: 0.5087 Epoch 40/1200, Train Loss: 0.6840, Train Accuracy: 0.5874, Val Loss: 0.6928, Val Accuracy: 0.5130 Epoch 41/1200, Train Loss: 0.6840, Train Accuracy: 0.5852, Val Loss: 0.6928, Val Accuracy: 0.5198 Epoch 42/1200, Train Loss: 0.6837, Train Accuracy: 0.5916, Val Loss: 0.6928, Val Accuracy: 0.5268 Epoch 43/1200, Train Loss: 0.6832, Train Accuracy: 0.5901, Val Loss: 0.6927, Val Accuracy: 0.5348 Epoch 44/1200, Train Loss: 0.6833, Train Accuracy: 0.5892, Val Loss: 0.6927, Val Accuracy: 0.5435 Epoch 45/1200, Train Loss: 0.6830, Train Accuracy: 0.5931, Val Loss: 0.6926, Val Accuracy: 0.5513 Epoch 46/1200, Train Loss: 0.6824, Train Accuracy: 0.5986, Val Loss: 0.6926, Val Accuracy: 0.5614 Epoch 47/1200, Train Loss: 0.6824, Train Accuracy: 0.5972, Val Loss: 0.6925, Val Accuracy: 0.5675 Epoch 48/1200, Train Loss: 0.6822, Train Accuracy: 0.5979, Val Loss: 0.6924, Val Accuracy: 0.5772 Epoch 49/1200, Train Loss: 0.6821, Train Accuracy: 0.5979, Val Loss: 0.6924, Val Accuracy: 0.5883 Epoch 50/1200, Train Loss: 0.6815, Train Accuracy: 0.6040, Val Loss: 0.6923, Val Accuracy: 0.5974 Epoch 51/1200, Train Loss: 0.6814, Train Accuracy: 0.6052, Val Loss: 0.6922, Val Accuracy: 0.6061 Epoch 52/1200, Train Loss: 0.6812, Train Accuracy: 0.6056, Val Loss: 0.6921, Val Accuracy: 0.6176 Epoch 53/1200, Train Loss: 0.6807, Train Accuracy: 0.6115, Val Loss: 0.6920, Val Accuracy: 0.6250 Epoch 54/1200, Train Loss: 0.6805, Train Accuracy: 0.6096, Val Loss: 0.6919, Val Accuracy: 0.6363 Epoch 55/1200, Train Loss: 0.6803, Train Accuracy: 0.6158, Val Loss: 0.6918, Val Accuracy: 0.6466 Epoch 56/1200, Train Loss: 0.6800, Train Accuracy: 0.6140, Val Loss: 0.6916, Val Accuracy: 0.6538 Epoch 57/1200, Train Loss: 0.6799, Train Accuracy: 0.6123, Val Loss: 0.6915, Val Accuracy: 0.6614 Epoch 58/1200, Train Loss: 0.6792, Train Accuracy: 0.6230, Val Loss: 0.6913, Val Accuracy: 0.6666 Epoch 59/1200, Train Loss: 0.6791, Train Accuracy: 0.6214, Val Loss: 0.6911, Val Accuracy: 0.6752 Epoch 60/1200, Train Loss: 0.6786, Train Accuracy: 0.6230, Val Loss: 0.6910, Val Accuracy: 0.6810 Epoch 61/1200, Train Loss: 0.6787, Train Accuracy: 0.6186, Val Loss: 0.6908, Val Accuracy: 0.6870 Epoch 62/1200, Train Loss: 0.6783, Train Accuracy: 0.6240, Val Loss: 0.6906, Val Accuracy: 0.6948 Epoch 63/1200, Train Loss: 0.6783, Train Accuracy: 0.6239, Val Loss: 0.6903, Val Accuracy: 0.7006 Epoch 64/1200, Train Loss: 0.6778, Train Accuracy: 0.6300, Val Loss: 0.6901, Val Accuracy: 0.7072 Epoch 65/1200, Train Loss: 0.6772, Train Accuracy: 0.6332, Val Loss: 0.6898, Val Accuracy: 0.7123 Epoch 66/1200, Train Loss: 0.6771, Train Accuracy: 0.6281, Val Loss: 0.6895, Val Accuracy: 0.7164 Epoch 67/1200, Train Loss: 0.6766, Train Accuracy: 0.6344, Val Loss: 0.6892, Val Accuracy: 0.7214 Epoch 68/1200, Train Loss: 0.6765, Train Accuracy: 0.6349, Val Loss: 0.6889, Val Accuracy: 0.7265 Epoch 69/1200, Train Loss: 0.6760, Train Accuracy: 0.6376, Val Loss: 0.6885, Val Accuracy: 0.7333 Epoch 70/1200, Train Loss: 0.6765, Train Accuracy: 0.6350, Val Loss: 0.6881, Val Accuracy: 0.7360 Epoch 71/1200, Train Loss: 0.6755, Train Accuracy: 0.6393, Val Loss: 0.6877, Val Accuracy: 0.7381 Epoch 72/1200, Train Loss: 0.6751, Train Accuracy: 0.6400, Val Loss: 0.6873, Val Accuracy: 0.7422 Epoch 73/1200, Train Loss: 0.6750, Train Accuracy: 0.6457, Val Loss: 0.6869, Val Accuracy: 0.7453 Epoch 74/1200, Train Loss: 0.6744, Train Accuracy: 0.6461, Val Loss: 0.6864, Val Accuracy: 0.7467 Epoch 75/1200, Train Loss: 0.6741, Train Accuracy: 0.6491, Val Loss: 0.6859, Val Accuracy: 0.7481 Epoch 76/1200, Train Loss: 0.6741, Train Accuracy: 0.6453, Val Loss: 0.6853, Val Accuracy: 0.7498 Epoch 77/1200, Train Loss: 0.6739, Train Accuracy: 0.6470, Val Loss: 0.6848, Val Accuracy: 0.7527 Epoch 78/1200, Train Loss: 0.6733, Train Accuracy: 0.6536, Val Loss: 0.6842, Val Accuracy: 0.7560 Epoch 79/1200, Train Loss: 0.6730, Train Accuracy: 0.6538, Val Loss: 0.6836, Val Accuracy: 0.7580 Epoch 80/1200, Train Loss: 0.6729, Train Accuracy: 0.6546, Val Loss: 0.6829, Val Accuracy: 0.7599 Epoch 81/1200, Train Loss: 0.6727, Train Accuracy: 0.6521, Val Loss: 0.6823, Val Accuracy: 0.7617 Epoch 82/1200, Train Loss: 0.6717, Train Accuracy: 0.6585, Val Loss: 0.6816, Val Accuracy: 0.7617 Epoch 83/1200, Train Loss: 0.6711, Train Accuracy: 0.6574, Val Loss: 0.6809, Val Accuracy: 0.7638 Epoch 84/1200, Train Loss: 0.6713, Train Accuracy: 0.6607, Val Loss: 0.6801, Val Accuracy: 0.7654 Epoch 85/1200, Train Loss: 0.6705, Train Accuracy: 0.6666, Val Loss: 0.6794, Val Accuracy: 0.7661 Epoch 86/1200, Train Loss: 0.6698, Train Accuracy: 0.6683, Val Loss: 0.6786, Val Accuracy: 0.7675 Epoch 87/1200, Train Loss: 0.6699, Train Accuracy: 0.6668, Val Loss: 0.6778, Val Accuracy: 0.7683 Epoch 88/1200, Train Loss: 0.6694, Train Accuracy: 0.6654, Val Loss: 0.6770, Val Accuracy: 0.7700 Epoch 89/1200, Train Loss: 0.6690, Train Accuracy: 0.6712, Val Loss: 0.6762, Val Accuracy: 0.7708 Epoch 90/1200, Train Loss: 0.6686, Train Accuracy: 0.6747, Val Loss: 0.6753, Val Accuracy: 0.7712 Epoch 91/1200, Train Loss: 0.6681, Train Accuracy: 0.6737, Val Loss: 0.6745, Val Accuracy: 0.7724 Epoch 92/1200, Train Loss: 0.6678, Train Accuracy: 0.6753, Val Loss: 0.6736, Val Accuracy: 0.7743 Epoch 93/1200, Train Loss: 0.6674, Train Accuracy: 0.6741, Val Loss: 0.6728, Val Accuracy: 0.7759 Epoch 94/1200, Train Loss: 0.6673, Train Accuracy: 0.6767, Val Loss: 0.6719, Val Accuracy: 0.7762 Epoch 95/1200, Train Loss: 0.6666, Train Accuracy: 0.6787, Val Loss: 0.6710, Val Accuracy: 0.7784 Epoch 96/1200, Train Loss: 0.6663, Train Accuracy: 0.6812, Val Loss: 0.6701, Val Accuracy: 0.7794 Epoch 97/1200, Train Loss: 0.6662, Train Accuracy: 0.6741, Val Loss: 0.6693, Val Accuracy: 0.7790 Epoch 98/1200, Train Loss: 0.6658, Train Accuracy: 0.6825, Val Loss: 0.6684, Val Accuracy: 0.7807 Epoch 99/1200, Train Loss: 0.6648, Train Accuracy: 0.6805, Val Loss: 0.6675, Val Accuracy: 0.7819 Epoch 100/1200, Train Loss: 0.6645, Train Accuracy: 0.6864, Val Loss: 0.6666, Val Accuracy: 0.7827 Epoch 101/1200, Train Loss: 0.6638, Train Accuracy: 0.6877, Val Loss: 0.6658, Val Accuracy: 0.7840 Epoch 102/1200, Train Loss: 0.6638, Train Accuracy: 0.6844, Val Loss: 0.6649, Val Accuracy: 0.7840 Epoch 103/1200, Train Loss: 0.6639, Train Accuracy: 0.6890, Val Loss: 0.6641, Val Accuracy: 0.7854 Epoch 104/1200, Train Loss: 0.6631, Train Accuracy: 0.6898, Val Loss: 0.6632, Val Accuracy: 0.7858 Epoch 105/1200, Train Loss: 0.6616, Train Accuracy: 0.6926, Val Loss: 0.6624, Val Accuracy: 0.7864 Epoch 106/1200, Train Loss: 0.6615, Train Accuracy: 0.6949, Val Loss: 0.6616, Val Accuracy: 0.7871 Epoch 107/1200, Train Loss: 0.6609, Train Accuracy: 0.6940, Val Loss: 0.6608, Val Accuracy: 0.7885 Epoch 108/1200, Train Loss: 0.6610, Train Accuracy: 0.6942, Val Loss: 0.6600, Val Accuracy: 0.7887 Epoch 109/1200, Train Loss: 0.6602, Train Accuracy: 0.7030, Val Loss: 0.6592, Val Accuracy: 0.7885 Epoch 110/1200, Train Loss: 0.6600, Train Accuracy: 0.7002, Val Loss: 0.6584, Val Accuracy: 0.7893 Epoch 111/1200, Train Loss: 0.6592, Train Accuracy: 0.7024, Val Loss: 0.6576, Val Accuracy: 0.7902 Epoch 112/1200, Train Loss: 0.6585, Train Accuracy: 0.7032, Val Loss: 0.6567, Val Accuracy: 0.7916 Epoch 113/1200, Train Loss: 0.6581, Train Accuracy: 0.7035, Val Loss: 0.6560, Val Accuracy: 0.7920 Epoch 114/1200, Train Loss: 0.6574, Train Accuracy: 0.7059, Val Loss: 0.6552, Val Accuracy: 0.7916 Epoch 115/1200, Train Loss: 0.6569, Train Accuracy: 0.7052, Val Loss: 0.6544, Val Accuracy: 0.7934 Epoch 116/1200, Train Loss: 0.6567, Train Accuracy: 0.7075, Val Loss: 0.6537, Val Accuracy: 0.7939 Epoch 117/1200, Train Loss: 0.6556, Train Accuracy: 0.7092, Val Loss: 0.6529, Val Accuracy: 0.7937 Epoch 118/1200, Train Loss: 0.6549, Train Accuracy: 0.7088, Val Loss: 0.6521, Val Accuracy: 0.7945 Epoch 119/1200, Train Loss: 0.6541, Train Accuracy: 0.7192, Val Loss: 0.6514, Val Accuracy: 0.7963 Epoch 120/1200, Train Loss: 0.6542, Train Accuracy: 0.7112, Val Loss: 0.6506, Val Accuracy: 0.7961 Epoch 121/1200, Train Loss: 0.6537, Train Accuracy: 0.7134, Val Loss: 0.6498, Val Accuracy: 0.7965 Epoch 122/1200, Train Loss: 0.6530, Train Accuracy: 0.7162, Val Loss: 0.6490, Val Accuracy: 0.7970 Epoch 123/1200, Train Loss: 0.6521, Train Accuracy: 0.7180, Val Loss: 0.6482, Val Accuracy: 0.7969 Epoch 124/1200, Train Loss: 0.6522, Train Accuracy: 0.7127, Val Loss: 0.6474, Val Accuracy: 0.7974 Epoch 125/1200, Train Loss: 0.6513, Train Accuracy: 0.7179, Val Loss: 0.6466, Val Accuracy: 0.7974 Epoch 126/1200, Train Loss: 0.6502, Train Accuracy: 0.7244, Val Loss: 0.6458, Val Accuracy: 0.7978 Epoch 127/1200, Train Loss: 0.6500, Train Accuracy: 0.7213, Val Loss: 0.6450, Val Accuracy: 0.7984 Epoch 128/1200, Train Loss: 0.6493, Train Accuracy: 0.7225, Val Loss: 0.6442, Val Accuracy: 0.7986 Epoch 129/1200, Train Loss: 0.6486, Train Accuracy: 0.7244, Val Loss: 0.6434, Val Accuracy: 0.7992 Epoch 130/1200, Train Loss: 0.6474, Train Accuracy: 0.7291, Val Loss: 0.6426, Val Accuracy: 0.7998 Epoch 131/1200, Train Loss: 0.6472, Train Accuracy: 0.7294, Val Loss: 0.6417, Val Accuracy: 0.8009 Epoch 132/1200, Train Loss: 0.6470, Train Accuracy: 0.7243, Val Loss: 0.6409, Val Accuracy: 0.8017 Epoch 133/1200, Train Loss: 0.6457, Train Accuracy: 0.7294, Val Loss: 0.6400, Val Accuracy: 0.8013 Epoch 134/1200, Train Loss: 0.6456, Train Accuracy: 0.7289, Val Loss: 0.6392, Val Accuracy: 0.8019 Epoch 135/1200, Train Loss: 0.6436, Train Accuracy: 0.7378, Val Loss: 0.6383, Val Accuracy: 0.8015 Epoch 136/1200, Train Loss: 0.6439, Train Accuracy: 0.7299, Val Loss: 0.6375, Val Accuracy: 0.8023 Epoch 137/1200, Train Loss: 0.6428, Train Accuracy: 0.7338, Val Loss: 0.6366, Val Accuracy: 0.8027 Epoch 138/1200, Train Loss: 0.6421, Train Accuracy: 0.7341, Val Loss: 0.6357, Val Accuracy: 0.8027 Epoch 139/1200, Train Loss: 0.6416, Train Accuracy: 0.7349, Val Loss: 0.6349, Val Accuracy: 0.8027 Epoch 140/1200, Train Loss: 0.6403, Train Accuracy: 0.7385, Val Loss: 0.6339, Val Accuracy: 0.8031 Epoch 141/1200, Train Loss: 0.6394, Train Accuracy: 0.7382, Val Loss: 0.6330, Val Accuracy: 0.8035 Epoch 142/1200, Train Loss: 0.6396, Train Accuracy: 0.7383, Val Loss: 0.6321, Val Accuracy: 0.8040 Epoch 143/1200, Train Loss: 0.6383, Train Accuracy: 0.7413, Val Loss: 0.6312, Val Accuracy: 0.8048 Epoch 144/1200, Train Loss: 0.6375, Train Accuracy: 0.7449, Val Loss: 0.6302, Val Accuracy: 0.8052 Epoch 145/1200, Train Loss: 0.6367, Train Accuracy: 0.7447, Val Loss: 0.6293, Val Accuracy: 0.8058 Epoch 146/1200, Train Loss: 0.6360, Train Accuracy: 0.7439, Val Loss: 0.6283, Val Accuracy: 0.8064 Epoch 147/1200, Train Loss: 0.6349, Train Accuracy: 0.7477, Val Loss: 0.6273, Val Accuracy: 0.8075 Epoch 148/1200, Train Loss: 0.6337, Train Accuracy: 0.7476, Val Loss: 0.6264, Val Accuracy: 0.8075 Epoch 149/1200, Train Loss: 0.6337, Train Accuracy: 0.7441, Val Loss: 0.6254, Val Accuracy: 0.8081 Epoch 150/1200, Train Loss: 0.6330, Train Accuracy: 0.7493, Val Loss: 0.6244, Val Accuracy: 0.8087 Epoch 151/1200, Train Loss: 0.6315, Train Accuracy: 0.7527, Val Loss: 0.6234, Val Accuracy: 0.8091 Epoch 152/1200, Train Loss: 0.6307, Train Accuracy: 0.7502, Val Loss: 0.6224, Val Accuracy: 0.8097 Epoch 153/1200, Train Loss: 0.6297, Train Accuracy: 0.7528, Val Loss: 0.6213, Val Accuracy: 0.8099 Epoch 154/1200, Train Loss: 0.6288, Train Accuracy: 0.7499, Val Loss: 0.6203, Val Accuracy: 0.8097 Epoch 155/1200, Train Loss: 0.6280, Train Accuracy: 0.7543, Val Loss: 0.6193, Val Accuracy: 0.8099 Epoch 156/1200, Train Loss: 0.6270, Train Accuracy: 0.7574, Val Loss: 0.6182, Val Accuracy: 0.8101 Epoch 157/1200, Train Loss: 0.6257, Train Accuracy: 0.7605, Val Loss: 0.6171, Val Accuracy: 0.8107 Epoch 158/1200, Train Loss: 0.6251, Train Accuracy: 0.7590, Val Loss: 0.6160, Val Accuracy: 0.8103 Epoch 159/1200, Train Loss: 0.6246, Train Accuracy: 0.7609, Val Loss: 0.6149, Val Accuracy: 0.8108 Epoch 160/1200, Train Loss: 0.6240, Train Accuracy: 0.7565, Val Loss: 0.6137, Val Accuracy: 0.8112 Epoch 161/1200, Train Loss: 0.6229, Train Accuracy: 0.7596, Val Loss: 0.6126, Val Accuracy: 0.8114 Epoch 162/1200, Train Loss: 0.6217, Train Accuracy: 0.7630, Val Loss: 0.6115, Val Accuracy: 0.8122 Epoch 163/1200, Train Loss: 0.6203, Train Accuracy: 0.7646, Val Loss: 0.6103, Val Accuracy: 0.8128 Epoch 164/1200, Train Loss: 0.6194, Train Accuracy: 0.7627, Val Loss: 0.6091, Val Accuracy: 0.8132 Epoch 165/1200, Train Loss: 0.6190, Train Accuracy: 0.7645, Val Loss: 0.6079, Val Accuracy: 0.8132 Epoch 166/1200, Train Loss: 0.6166, Train Accuracy: 0.7694, Val Loss: 0.6067, Val Accuracy: 0.8136 Epoch 167/1200, Train Loss: 0.6163, Train Accuracy: 0.7664, Val Loss: 0.6055, Val Accuracy: 0.8134 Epoch 168/1200, Train Loss: 0.6149, Train Accuracy: 0.7660, Val Loss: 0.6043, Val Accuracy: 0.8142 Epoch 169/1200, Train Loss: 0.6139, Train Accuracy: 0.7692, Val Loss: 0.6031, Val Accuracy: 0.8142 Epoch 170/1200, Train Loss: 0.6130, Train Accuracy: 0.7699, Val Loss: 0.6018, Val Accuracy: 0.8145 Epoch 171/1200, Train Loss: 0.6120, Train Accuracy: 0.7701, Val Loss: 0.6006, Val Accuracy: 0.8151 Epoch 172/1200, Train Loss: 0.6104, Train Accuracy: 0.7732, Val Loss: 0.5993, Val Accuracy: 0.8153 Epoch 173/1200, Train Loss: 0.6094, Train Accuracy: 0.7715, Val Loss: 0.5980, Val Accuracy: 0.8151 Epoch 174/1200, Train Loss: 0.6080, Train Accuracy: 0.7751, Val Loss: 0.5967, Val Accuracy: 0.8155 Epoch 175/1200, Train Loss: 0.6075, Train Accuracy: 0.7758, Val Loss: 0.5954, Val Accuracy: 0.8157 Epoch 176/1200, Train Loss: 0.6064, Train Accuracy: 0.7727, Val Loss: 0.5941, Val Accuracy: 0.8161 Epoch 177/1200, Train Loss: 0.6055, Train Accuracy: 0.7768, Val Loss: 0.5928, Val Accuracy: 0.8165 Epoch 178/1200, Train Loss: 0.6046, Train Accuracy: 0.7752, Val Loss: 0.5914, Val Accuracy: 0.8169 Epoch 179/1200, Train Loss: 0.6029, Train Accuracy: 0.7743, Val Loss: 0.5901, Val Accuracy: 0.8171 Epoch 180/1200, Train Loss: 0.6016, Train Accuracy: 0.7769, Val Loss: 0.5887, Val Accuracy: 0.8173 Epoch 181/1200, Train Loss: 0.6005, Train Accuracy: 0.7771, Val Loss: 0.5874, Val Accuracy: 0.8180 Epoch 182/1200, Train Loss: 0.5988, Train Accuracy: 0.7829, Val Loss: 0.5860, Val Accuracy: 0.8182 Epoch 183/1200, Train Loss: 0.5978, Train Accuracy: 0.7821, Val Loss: 0.5846, Val Accuracy: 0.8184 Epoch 184/1200, Train Loss: 0.5969, Train Accuracy: 0.7844, Val Loss: 0.5832, Val Accuracy: 0.8186 Epoch 185/1200, Train Loss: 0.5958, Train Accuracy: 0.7823, Val Loss: 0.5817, Val Accuracy: 0.8186 Epoch 186/1200, Train Loss: 0.5939, Train Accuracy: 0.7850, Val Loss: 0.5803, Val Accuracy: 0.8198 Epoch 187/1200, Train Loss: 0.5926, Train Accuracy: 0.7875, Val Loss: 0.5789, Val Accuracy: 0.8202 Epoch 188/1200, Train Loss: 0.5915, Train Accuracy: 0.7833, Val Loss: 0.5775, Val Accuracy: 0.8204 Epoch 189/1200, Train Loss: 0.5899, Train Accuracy: 0.7867, Val Loss: 0.5760, Val Accuracy: 0.8208 Epoch 190/1200, Train Loss: 0.5886, Train Accuracy: 0.7882, Val Loss: 0.5746, Val Accuracy: 0.8213 Epoch 191/1200, Train Loss: 0.5879, Train Accuracy: 0.7883, Val Loss: 0.5731, Val Accuracy: 0.8215 Epoch 192/1200, Train Loss: 0.5862, Train Accuracy: 0.7885, Val Loss: 0.5716, Val Accuracy: 0.8221 Epoch 193/1200, Train Loss: 0.5850, Train Accuracy: 0.7875, Val Loss: 0.5700, Val Accuracy: 0.8229 Epoch 194/1200, Train Loss: 0.5841, Train Accuracy: 0.7893, Val Loss: 0.5685, Val Accuracy: 0.8229 Epoch 195/1200, Train Loss: 0.5823, Train Accuracy: 0.7904, Val Loss: 0.5670, Val Accuracy: 0.8229 Epoch 196/1200, Train Loss: 0.5813, Train Accuracy: 0.7922, Val Loss: 0.5654, Val Accuracy: 0.8229 Epoch 197/1200, Train Loss: 0.5793, Train Accuracy: 0.7958, Val Loss: 0.5638, Val Accuracy: 0.8229 Epoch 198/1200, Train Loss: 0.5785, Train Accuracy: 0.7910, Val Loss: 0.5623, Val Accuracy: 0.8233 Epoch 199/1200, Train Loss: 0.5768, Train Accuracy: 0.7936, Val Loss: 0.5607, Val Accuracy: 0.8241 Epoch 200/1200, Train Loss: 0.5752, Train Accuracy: 0.7956, Val Loss: 0.5591, Val Accuracy: 0.8247 Epoch 201/1200, Train Loss: 0.5744, Train Accuracy: 0.7937, Val Loss: 0.5575, Val Accuracy: 0.8262 Epoch 202/1200, Train Loss: 0.5722, Train Accuracy: 0.7937, Val Loss: 0.5560, Val Accuracy: 0.8266 Epoch 203/1200, Train Loss: 0.5710, Train Accuracy: 0.7975, Val Loss: 0.5543, Val Accuracy: 0.8268 Epoch 204/1200, Train Loss: 0.5702, Train Accuracy: 0.7981, Val Loss: 0.5527, Val Accuracy: 0.8270 Epoch 205/1200, Train Loss: 0.5679, Train Accuracy: 0.7980, Val Loss: 0.5511, Val Accuracy: 0.8276 Epoch 206/1200, Train Loss: 0.5669, Train Accuracy: 0.7974, Val Loss: 0.5495, Val Accuracy: 0.8276 Epoch 207/1200, Train Loss: 0.5661, Train Accuracy: 0.8003, Val Loss: 0.5478, Val Accuracy: 0.8289 Epoch 208/1200, Train Loss: 0.5645, Train Accuracy: 0.8012, Val Loss: 0.5462, Val Accuracy: 0.8297 Epoch 209/1200, Train Loss: 0.5627, Train Accuracy: 0.7988, Val Loss: 0.5446, Val Accuracy: 0.8299 Epoch 210/1200, Train Loss: 0.5601, Train Accuracy: 0.8026, Val Loss: 0.5429, Val Accuracy: 0.8297 Epoch 211/1200, Train Loss: 0.5592, Train Accuracy: 0.8082, Val Loss: 0.5413, Val Accuracy: 0.8299 Epoch 212/1200, Train Loss: 0.5578, Train Accuracy: 0.8017, Val Loss: 0.5395, Val Accuracy: 0.8303 Epoch 213/1200, Train Loss: 0.5561, Train Accuracy: 0.8060, Val Loss: 0.5378, Val Accuracy: 0.8307 Epoch 214/1200, Train Loss: 0.5541, Train Accuracy: 0.8082, Val Loss: 0.5361, Val Accuracy: 0.8311 Epoch 215/1200, Train Loss: 0.5524, Train Accuracy: 0.8079, Val Loss: 0.5344, Val Accuracy: 0.8320 Epoch 216/1200, Train Loss: 0.5511, Train Accuracy: 0.8074, Val Loss: 0.5327, Val Accuracy: 0.8326 Epoch 217/1200, Train Loss: 0.5501, Train Accuracy: 0.8096, Val Loss: 0.5310, Val Accuracy: 0.8332 Epoch 218/1200, Train Loss: 0.5491, Train Accuracy: 0.8095, Val Loss: 0.5293, Val Accuracy: 0.8336 Epoch 219/1200, Train Loss: 0.5468, Train Accuracy: 0.8097, Val Loss: 0.5276, Val Accuracy: 0.8340 Epoch 220/1200, Train Loss: 0.5449, Train Accuracy: 0.8095, Val Loss: 0.5258, Val Accuracy: 0.8344 Epoch 221/1200, Train Loss: 0.5431, Train Accuracy: 0.8122, Val Loss: 0.5240, Val Accuracy: 0.8348 Epoch 222/1200, Train Loss: 0.5413, Train Accuracy: 0.8122, Val Loss: 0.5223, Val Accuracy: 0.8351 Epoch 223/1200, Train Loss: 0.5404, Train Accuracy: 0.8100, Val Loss: 0.5205, Val Accuracy: 0.8359 Epoch 224/1200, Train Loss: 0.5398, Train Accuracy: 0.8131, Val Loss: 0.5188, Val Accuracy: 0.8361 Epoch 225/1200, Train Loss: 0.5368, Train Accuracy: 0.8144, Val Loss: 0.5170, Val Accuracy: 0.8365 Epoch 226/1200, Train Loss: 0.5355, Train Accuracy: 0.8152, Val Loss: 0.5152, Val Accuracy: 0.8371 Epoch 227/1200, Train Loss: 0.5334, Train Accuracy: 0.8176, Val Loss: 0.5134, Val Accuracy: 0.8375 Epoch 228/1200, Train Loss: 0.5314, Train Accuracy: 0.8164, Val Loss: 0.5117, Val Accuracy: 0.8383 Epoch 229/1200, Train Loss: 0.5306, Train Accuracy: 0.8158, Val Loss: 0.5099, Val Accuracy: 0.8388 Epoch 230/1200, Train Loss: 0.5286, Train Accuracy: 0.8183, Val Loss: 0.5080, Val Accuracy: 0.8396 Epoch 231/1200, Train Loss: 0.5266, Train Accuracy: 0.8201, Val Loss: 0.5062, Val Accuracy: 0.8400 Epoch 232/1200, Train Loss: 0.5255, Train Accuracy: 0.8195, Val Loss: 0.5043, Val Accuracy: 0.8402 Epoch 233/1200, Train Loss: 0.5238, Train Accuracy: 0.8194, Val Loss: 0.5025, Val Accuracy: 0.8408 Epoch 234/1200, Train Loss: 0.5219, Train Accuracy: 0.8184, Val Loss: 0.5007, Val Accuracy: 0.8414 Epoch 235/1200, Train Loss: 0.5201, Train Accuracy: 0.8222, Val Loss: 0.4989, Val Accuracy: 0.8416 Epoch 236/1200, Train Loss: 0.5192, Train Accuracy: 0.8219, Val Loss: 0.4970, Val Accuracy: 0.8416 Epoch 237/1200, Train Loss: 0.5159, Train Accuracy: 0.8240, Val Loss: 0.4952, Val Accuracy: 0.8421 Epoch 238/1200, Train Loss: 0.5152, Train Accuracy: 0.8231, Val Loss: 0.4934, Val Accuracy: 0.8421 Epoch 239/1200, Train Loss: 0.5131, Train Accuracy: 0.8231, Val Loss: 0.4915, Val Accuracy: 0.8427 Epoch 240/1200, Train Loss: 0.5113, Train Accuracy: 0.8271, Val Loss: 0.4897, Val Accuracy: 0.8429 Epoch 241/1200, Train Loss: 0.5098, Train Accuracy: 0.8224, Val Loss: 0.4878, Val Accuracy: 0.8435 Epoch 242/1200, Train Loss: 0.5081, Train Accuracy: 0.8262, Val Loss: 0.4859, Val Accuracy: 0.8445 Epoch 243/1200, Train Loss: 0.5059, Train Accuracy: 0.8260, Val Loss: 0.4841, Val Accuracy: 0.8456 Epoch 244/1200, Train Loss: 0.5050, Train Accuracy: 0.8262, Val Loss: 0.4821, Val Accuracy: 0.8462 Epoch 245/1200, Train Loss: 0.5031, Train Accuracy: 0.8306, Val Loss: 0.4803, Val Accuracy: 0.8474 Epoch 246/1200, Train Loss: 0.5007, Train Accuracy: 0.8305, Val Loss: 0.4784, Val Accuracy: 0.8482 Epoch 247/1200, Train Loss: 0.4993, Train Accuracy: 0.8307, Val Loss: 0.4766, Val Accuracy: 0.8488 Epoch 248/1200, Train Loss: 0.4974, Train Accuracy: 0.8307, Val Loss: 0.4747, Val Accuracy: 0.8491 Epoch 249/1200, Train Loss: 0.4962, Train Accuracy: 0.8303, Val Loss: 0.4728, Val Accuracy: 0.8497 Epoch 250/1200, Train Loss: 0.4924, Train Accuracy: 0.8335, Val Loss: 0.4709, Val Accuracy: 0.8503 Epoch 251/1200, Train Loss: 0.4920, Train Accuracy: 0.8324, Val Loss: 0.4690, Val Accuracy: 0.8513 Epoch 252/1200, Train Loss: 0.4905, Train Accuracy: 0.8355, Val Loss: 0.4671, Val Accuracy: 0.8521 Epoch 253/1200, Train Loss: 0.4874, Train Accuracy: 0.8349, Val Loss: 0.4652, Val Accuracy: 0.8528 Epoch 254/1200, Train Loss: 0.4862, Train Accuracy: 0.8332, Val Loss: 0.4634, Val Accuracy: 0.8534 Epoch 255/1200, Train Loss: 0.4838, Train Accuracy: 0.8370, Val Loss: 0.4615, Val Accuracy: 0.8544 Epoch 256/1200, Train Loss: 0.4829, Train Accuracy: 0.8373, Val Loss: 0.4595, Val Accuracy: 0.8542 Epoch 257/1200, Train Loss: 0.4807, Train Accuracy: 0.8372, Val Loss: 0.4577, Val Accuracy: 0.8546 Epoch 258/1200, Train Loss: 0.4785, Train Accuracy: 0.8381, Val Loss: 0.4557, Val Accuracy: 0.8550 Epoch 259/1200, Train Loss: 0.4777, Train Accuracy: 0.8385, Val Loss: 0.4538, Val Accuracy: 0.8556 Epoch 260/1200, Train Loss: 0.4753, Train Accuracy: 0.8396, Val Loss: 0.4518, Val Accuracy: 0.8565 Epoch 261/1200, Train Loss: 0.4725, Train Accuracy: 0.8412, Val Loss: 0.4499, Val Accuracy: 0.8581 Epoch 262/1200, Train Loss: 0.4714, Train Accuracy: 0.8427, Val Loss: 0.4479, Val Accuracy: 0.8593 Epoch 263/1200, Train Loss: 0.4682, Train Accuracy: 0.8437, Val Loss: 0.4460, Val Accuracy: 0.8600 Epoch 264/1200, Train Loss: 0.4681, Train Accuracy: 0.8435, Val Loss: 0.4440, Val Accuracy: 0.8606 Epoch 265/1200, Train Loss: 0.4656, Train Accuracy: 0.8434, Val Loss: 0.4421, Val Accuracy: 0.8618 Epoch 266/1200, Train Loss: 0.4631, Train Accuracy: 0.8442, Val Loss: 0.4402, Val Accuracy: 0.8631 Epoch 267/1200, Train Loss: 0.4616, Train Accuracy: 0.8454, Val Loss: 0.4382, Val Accuracy: 0.8629 Epoch 268/1200, Train Loss: 0.4600, Train Accuracy: 0.8478, Val Loss: 0.4362, Val Accuracy: 0.8643 Epoch 269/1200, Train Loss: 0.4570, Train Accuracy: 0.8488, Val Loss: 0.4343, Val Accuracy: 0.8655 Epoch 270/1200, Train Loss: 0.4568, Train Accuracy: 0.8496, Val Loss: 0.4323, Val Accuracy: 0.8661 Epoch 271/1200, Train Loss: 0.4536, Train Accuracy: 0.8489, Val Loss: 0.4303, Val Accuracy: 0.8663 Epoch 272/1200, Train Loss: 0.4525, Train Accuracy: 0.8501, Val Loss: 0.4283, Val Accuracy: 0.8676 Epoch 273/1200, Train Loss: 0.4495, Train Accuracy: 0.8506, Val Loss: 0.4264, Val Accuracy: 0.8684 Epoch 274/1200, Train Loss: 0.4477, Train Accuracy: 0.8522, Val Loss: 0.4244, Val Accuracy: 0.8703 Epoch 275/1200, Train Loss: 0.4459, Train Accuracy: 0.8538, Val Loss: 0.4224, Val Accuracy: 0.8707 Epoch 276/1200, Train Loss: 0.4440, Train Accuracy: 0.8554, Val Loss: 0.4204, Val Accuracy: 0.8715 Epoch 277/1200, Train Loss: 0.4415, Train Accuracy: 0.8551, Val Loss: 0.4183, Val Accuracy: 0.8721 Epoch 278/1200, Train Loss: 0.4413, Train Accuracy: 0.8521, Val Loss: 0.4164, Val Accuracy: 0.8725 Epoch 279/1200, Train Loss: 0.4387, Train Accuracy: 0.8560, Val Loss: 0.4144, Val Accuracy: 0.8733 Epoch 280/1200, Train Loss: 0.4365, Train Accuracy: 0.8565, Val Loss: 0.4124, Val Accuracy: 0.8734 Epoch 281/1200, Train Loss: 0.4335, Train Accuracy: 0.8605, Val Loss: 0.4103, Val Accuracy: 0.8736 Epoch 282/1200, Train Loss: 0.4324, Train Accuracy: 0.8587, Val Loss: 0.4083, Val Accuracy: 0.8744 Epoch 283/1200, Train Loss: 0.4302, Train Accuracy: 0.8597, Val Loss: 0.4063, Val Accuracy: 0.8760 Epoch 284/1200, Train Loss: 0.4286, Train Accuracy: 0.8593, Val Loss: 0.4044, Val Accuracy: 0.8773 Epoch 285/1200, Train Loss: 0.4268, Train Accuracy: 0.8602, Val Loss: 0.4024, Val Accuracy: 0.8775 Epoch 286/1200, Train Loss: 0.4246, Train Accuracy: 0.8609, Val Loss: 0.4004, Val Accuracy: 0.8787 Epoch 287/1200, Train Loss: 0.4228, Train Accuracy: 0.8603, Val Loss: 0.3984, Val Accuracy: 0.8787 Epoch 288/1200, Train Loss: 0.4214, Train Accuracy: 0.8619, Val Loss: 0.3964, Val Accuracy: 0.8793 Epoch 289/1200, Train Loss: 0.4187, Train Accuracy: 0.8628, Val Loss: 0.3943, Val Accuracy: 0.8804 Epoch 290/1200, Train Loss: 0.4160, Train Accuracy: 0.8668, Val Loss: 0.3924, Val Accuracy: 0.8806 Epoch 291/1200, Train Loss: 0.4135, Train Accuracy: 0.8667, Val Loss: 0.3903, Val Accuracy: 0.8818 Epoch 292/1200, Train Loss: 0.4118, Train Accuracy: 0.8668, Val Loss: 0.3883, Val Accuracy: 0.8832 Epoch 293/1200, Train Loss: 0.4102, Train Accuracy: 0.8676, Val Loss: 0.3863, Val Accuracy: 0.8837 Epoch 294/1200, Train Loss: 0.4083, Train Accuracy: 0.8664, Val Loss: 0.3842, Val Accuracy: 0.8847 Epoch 295/1200, Train Loss: 0.4058, Train Accuracy: 0.8691, Val Loss: 0.3823, Val Accuracy: 0.8855 Epoch 296/1200, Train Loss: 0.4041, Train Accuracy: 0.8694, Val Loss: 0.3802, Val Accuracy: 0.8857 Epoch 297/1200, Train Loss: 0.4039, Train Accuracy: 0.8681, Val Loss: 0.3782, Val Accuracy: 0.8865 Epoch 298/1200, Train Loss: 0.4002, Train Accuracy: 0.8726, Val Loss: 0.3761, Val Accuracy: 0.8874 Epoch 299/1200, Train Loss: 0.3982, Train Accuracy: 0.8740, Val Loss: 0.3741, Val Accuracy: 0.8884 Epoch 300/1200, Train Loss: 0.3957, Train Accuracy: 0.8764, Val Loss: 0.3721, Val Accuracy: 0.8886 Epoch 301/1200, Train Loss: 0.3947, Train Accuracy: 0.8736, Val Loss: 0.3701, Val Accuracy: 0.8896 Epoch 302/1200, Train Loss: 0.3912, Train Accuracy: 0.8754, Val Loss: 0.3680, Val Accuracy: 0.8898 Epoch 303/1200, Train Loss: 0.3892, Train Accuracy: 0.8776, Val Loss: 0.3660, Val Accuracy: 0.8904 Epoch 304/1200, Train Loss: 0.3882, Train Accuracy: 0.8774, Val Loss: 0.3639, Val Accuracy: 0.8915 Epoch 305/1200, Train Loss: 0.3847, Train Accuracy: 0.8798, Val Loss: 0.3618, Val Accuracy: 0.8921 Epoch 306/1200, Train Loss: 0.3826, Train Accuracy: 0.8803, Val Loss: 0.3598, Val Accuracy: 0.8927 Epoch 307/1200, Train Loss: 0.3810, Train Accuracy: 0.8792, Val Loss: 0.3578, Val Accuracy: 0.8937 Epoch 308/1200, Train Loss: 0.3777, Train Accuracy: 0.8804, Val Loss: 0.3558, Val Accuracy: 0.8944 Epoch 309/1200, Train Loss: 0.3774, Train Accuracy: 0.8817, Val Loss: 0.3538, Val Accuracy: 0.8958 Epoch 310/1200, Train Loss: 0.3753, Train Accuracy: 0.8808, Val Loss: 0.3518, Val Accuracy: 0.8964 Epoch 311/1200, Train Loss: 0.3730, Train Accuracy: 0.8816, Val Loss: 0.3499, Val Accuracy: 0.8970 Epoch 312/1200, Train Loss: 0.3704, Train Accuracy: 0.8837, Val Loss: 0.3479, Val Accuracy: 0.8970 Epoch 313/1200, Train Loss: 0.3680, Train Accuracy: 0.8859, Val Loss: 0.3458, Val Accuracy: 0.8974 Epoch 314/1200, Train Loss: 0.3681, Train Accuracy: 0.8831, Val Loss: 0.3439, Val Accuracy: 0.8979 Epoch 315/1200, Train Loss: 0.3652, Train Accuracy: 0.8868, Val Loss: 0.3419, Val Accuracy: 0.8985 Epoch 316/1200, Train Loss: 0.3624, Train Accuracy: 0.8863, Val Loss: 0.3400, Val Accuracy: 0.8985 Epoch 317/1200, Train Loss: 0.3613, Train Accuracy: 0.8882, Val Loss: 0.3380, Val Accuracy: 0.8995 Epoch 318/1200, Train Loss: 0.3581, Train Accuracy: 0.8901, Val Loss: 0.3361, Val Accuracy: 0.9005 Epoch 319/1200, Train Loss: 0.3559, Train Accuracy: 0.8880, Val Loss: 0.3341, Val Accuracy: 0.9014 Epoch 320/1200, Train Loss: 0.3542, Train Accuracy: 0.8922, Val Loss: 0.3321, Val Accuracy: 0.9026 Epoch 321/1200, Train Loss: 0.3517, Train Accuracy: 0.8902, Val Loss: 0.3301, Val Accuracy: 0.9032 Epoch 322/1200, Train Loss: 0.3503, Train Accuracy: 0.8935, Val Loss: 0.3281, Val Accuracy: 0.9040 Epoch 323/1200, Train Loss: 0.3469, Train Accuracy: 0.8933, Val Loss: 0.3261, Val Accuracy: 0.9049 Epoch 324/1200, Train Loss: 0.3469, Train Accuracy: 0.8929, Val Loss: 0.3242, Val Accuracy: 0.9061 Epoch 325/1200, Train Loss: 0.3452, Train Accuracy: 0.8957, Val Loss: 0.3222, Val Accuracy: 0.9065 Epoch 326/1200, Train Loss: 0.3425, Train Accuracy: 0.8959, Val Loss: 0.3202, Val Accuracy: 0.9075 Epoch 327/1200, Train Loss: 0.3415, Train Accuracy: 0.8930, Val Loss: 0.3183, Val Accuracy: 0.9082 Epoch 328/1200, Train Loss: 0.3380, Train Accuracy: 0.8975, Val Loss: 0.3164, Val Accuracy: 0.9086 Epoch 329/1200, Train Loss: 0.3362, Train Accuracy: 0.8981, Val Loss: 0.3144, Val Accuracy: 0.9092 Epoch 330/1200, Train Loss: 0.3332, Train Accuracy: 0.8987, Val Loss: 0.3124, Val Accuracy: 0.9098 Epoch 331/1200, Train Loss: 0.3320, Train Accuracy: 0.8999, Val Loss: 0.3105, Val Accuracy: 0.9098 Epoch 332/1200, Train Loss: 0.3327, Train Accuracy: 0.8970, Val Loss: 0.3086, Val Accuracy: 0.9108 Epoch 333/1200, Train Loss: 0.3285, Train Accuracy: 0.8979, Val Loss: 0.3067, Val Accuracy: 0.9110 Epoch 334/1200, Train Loss: 0.3268, Train Accuracy: 0.9003, Val Loss: 0.3049, Val Accuracy: 0.9110 Epoch 335/1200, Train Loss: 0.3242, Train Accuracy: 0.9007, Val Loss: 0.3030, Val Accuracy: 0.9117 Epoch 336/1200, Train Loss: 0.3222, Train Accuracy: 0.9006, Val Loss: 0.3012, Val Accuracy: 0.9127 Epoch 337/1200, Train Loss: 0.3212, Train Accuracy: 0.9032, Val Loss: 0.2994, Val Accuracy: 0.9133 Epoch 338/1200, Train Loss: 0.3177, Train Accuracy: 0.9029, Val Loss: 0.2975, Val Accuracy: 0.9135 Epoch 339/1200, Train Loss: 0.3169, Train Accuracy: 0.9026, Val Loss: 0.2958, Val Accuracy: 0.9139 Epoch 340/1200, Train Loss: 0.3142, Train Accuracy: 0.9056, Val Loss: 0.2940, Val Accuracy: 0.9137 Epoch 341/1200, Train Loss: 0.3123, Train Accuracy: 0.9060, Val Loss: 0.2922, Val Accuracy: 0.9145 Epoch 342/1200, Train Loss: 0.3125, Train Accuracy: 0.9062, Val Loss: 0.2905, Val Accuracy: 0.9147 Epoch 343/1200, Train Loss: 0.3100, Train Accuracy: 0.9063, Val Loss: 0.2887, Val Accuracy: 0.9147 Epoch 344/1200, Train Loss: 0.3095, Train Accuracy: 0.9044, Val Loss: 0.2869, Val Accuracy: 0.9152 Epoch 345/1200, Train Loss: 0.3062, Train Accuracy: 0.9077, Val Loss: 0.2852, Val Accuracy: 0.9158 Epoch 346/1200, Train Loss: 0.3049, Train Accuracy: 0.9069, Val Loss: 0.2835, Val Accuracy: 0.9164 Epoch 347/1200, Train Loss: 0.3027, Train Accuracy: 0.9083, Val Loss: 0.2817, Val Accuracy: 0.9168 Epoch 348/1200, Train Loss: 0.3006, Train Accuracy: 0.9086, Val Loss: 0.2800, Val Accuracy: 0.9172 Epoch 349/1200, Train Loss: 0.2999, Train Accuracy: 0.9088, Val Loss: 0.2784, Val Accuracy: 0.9174 Epoch 350/1200, Train Loss: 0.2975, Train Accuracy: 0.9109, Val Loss: 0.2767, Val Accuracy: 0.9178 Epoch 351/1200, Train Loss: 0.2952, Train Accuracy: 0.9096, Val Loss: 0.2751, Val Accuracy: 0.9184 Epoch 352/1200, Train Loss: 0.2943, Train Accuracy: 0.9099, Val Loss: 0.2735, Val Accuracy: 0.9184 Epoch 353/1200, Train Loss: 0.2933, Train Accuracy: 0.9107, Val Loss: 0.2718, Val Accuracy: 0.9184 Epoch 354/1200, Train Loss: 0.2905, Train Accuracy: 0.9100, Val Loss: 0.2702, Val Accuracy: 0.9191 Epoch 355/1200, Train Loss: 0.2892, Train Accuracy: 0.9123, Val Loss: 0.2687, Val Accuracy: 0.9191 Epoch 356/1200, Train Loss: 0.2875, Train Accuracy: 0.9116, Val Loss: 0.2671, Val Accuracy: 0.9197 Epoch 357/1200, Train Loss: 0.2865, Train Accuracy: 0.9120, Val Loss: 0.2656, Val Accuracy: 0.9197 Epoch 358/1200, Train Loss: 0.2846, Train Accuracy: 0.9150, Val Loss: 0.2641, Val Accuracy: 0.9207 Epoch 359/1200, Train Loss: 0.2819, Train Accuracy: 0.9163, Val Loss: 0.2626, Val Accuracy: 0.9203 Epoch 360/1200, Train Loss: 0.2814, Train Accuracy: 0.9144, Val Loss: 0.2611, Val Accuracy: 0.9203 Epoch 361/1200, Train Loss: 0.2777, Train Accuracy: 0.9150, Val Loss: 0.2596, Val Accuracy: 0.9211 Epoch 362/1200, Train Loss: 0.2772, Train Accuracy: 0.9138, Val Loss: 0.2581, Val Accuracy: 0.9213 Epoch 363/1200, Train Loss: 0.2744, Train Accuracy: 0.9184, Val Loss: 0.2566, Val Accuracy: 0.9215 Epoch 364/1200, Train Loss: 0.2750, Train Accuracy: 0.9166, Val Loss: 0.2551, Val Accuracy: 0.9215 Epoch 365/1200, Train Loss: 0.2714, Train Accuracy: 0.9194, Val Loss: 0.2536, Val Accuracy: 0.9211 Epoch 366/1200, Train Loss: 0.2698, Train Accuracy: 0.9190, Val Loss: 0.2522, Val Accuracy: 0.9211 Epoch 367/1200, Train Loss: 0.2707, Train Accuracy: 0.9168, Val Loss: 0.2508, Val Accuracy: 0.9215 Epoch 368/1200, Train Loss: 0.2678, Train Accuracy: 0.9178, Val Loss: 0.2494, Val Accuracy: 0.9219 Epoch 369/1200, Train Loss: 0.2661, Train Accuracy: 0.9178, Val Loss: 0.2480, Val Accuracy: 0.9219 Epoch 370/1200, Train Loss: 0.2656, Train Accuracy: 0.9191, Val Loss: 0.2466, Val Accuracy: 0.9224 Epoch 371/1200, Train Loss: 0.2631, Train Accuracy: 0.9200, Val Loss: 0.2453, Val Accuracy: 0.9224 Epoch 372/1200, Train Loss: 0.2640, Train Accuracy: 0.9176, Val Loss: 0.2440, Val Accuracy: 0.9224 Epoch 373/1200, Train Loss: 0.2614, Train Accuracy: 0.9197, Val Loss: 0.2427, Val Accuracy: 0.9224 Epoch 374/1200, Train Loss: 0.2604, Train Accuracy: 0.9180, Val Loss: 0.2414, Val Accuracy: 0.9228 Epoch 375/1200, Train Loss: 0.2587, Train Accuracy: 0.9204, Val Loss: 0.2402, Val Accuracy: 0.9226 Epoch 376/1200, Train Loss: 0.2593, Train Accuracy: 0.9195, Val Loss: 0.2389, Val Accuracy: 0.9226 Epoch 377/1200, Train Loss: 0.2552, Train Accuracy: 0.9231, Val Loss: 0.2376, Val Accuracy: 0.9230 Epoch 378/1200, Train Loss: 0.2546, Train Accuracy: 0.9205, Val Loss: 0.2365, Val Accuracy: 0.9238 Epoch 379/1200, Train Loss: 0.2531, Train Accuracy: 0.9211, Val Loss: 0.2352, Val Accuracy: 0.9238 Epoch 380/1200, Train Loss: 0.2517, Train Accuracy: 0.9207, Val Loss: 0.2341, Val Accuracy: 0.9246 Epoch 381/1200, Train Loss: 0.2505, Train Accuracy: 0.9221, Val Loss: 0.2329, Val Accuracy: 0.9250 Epoch 382/1200, Train Loss: 0.2499, Train Accuracy: 0.9219, Val Loss: 0.2317, Val Accuracy: 0.9248 Epoch 383/1200, Train Loss: 0.2473, Train Accuracy: 0.9243, Val Loss: 0.2305, Val Accuracy: 0.9248 Epoch 384/1200, Train Loss: 0.2451, Train Accuracy: 0.9225, Val Loss: 0.2294, Val Accuracy: 0.9253 Epoch 385/1200, Train Loss: 0.2453, Train Accuracy: 0.9248, Val Loss: 0.2283, Val Accuracy: 0.9255 Epoch 386/1200, Train Loss: 0.2446, Train Accuracy: 0.9226, Val Loss: 0.2272, Val Accuracy: 0.9255 Epoch 387/1200, Train Loss: 0.2415, Train Accuracy: 0.9264, Val Loss: 0.2261, Val Accuracy: 0.9255 Epoch 388/1200, Train Loss: 0.2418, Train Accuracy: 0.9247, Val Loss: 0.2250, Val Accuracy: 0.9257 Epoch 389/1200, Train Loss: 0.2393, Train Accuracy: 0.9263, Val Loss: 0.2239, Val Accuracy: 0.9261 Epoch 390/1200, Train Loss: 0.2396, Train Accuracy: 0.9257, Val Loss: 0.2228, Val Accuracy: 0.9265 Epoch 391/1200, Train Loss: 0.2382, Train Accuracy: 0.9245, Val Loss: 0.2218, Val Accuracy: 0.9269 Epoch 392/1200, Train Loss: 0.2370, Train Accuracy: 0.9265, Val Loss: 0.2208, Val Accuracy: 0.9269 Epoch 393/1200, Train Loss: 0.2357, Train Accuracy: 0.9270, Val Loss: 0.2198, Val Accuracy: 0.9275 Epoch 394/1200, Train Loss: 0.2336, Train Accuracy: 0.9275, Val Loss: 0.2188, Val Accuracy: 0.9277 Epoch 395/1200, Train Loss: 0.2339, Train Accuracy: 0.9261, Val Loss: 0.2178, Val Accuracy: 0.9275 Epoch 396/1200, Train Loss: 0.2335, Train Accuracy: 0.9269, Val Loss: 0.2169, Val Accuracy: 0.9273 Epoch 397/1200, Train Loss: 0.2304, Train Accuracy: 0.9293, Val Loss: 0.2159, Val Accuracy: 0.9273 Epoch 398/1200, Train Loss: 0.2307, Train Accuracy: 0.9281, Val Loss: 0.2150, Val Accuracy: 0.9277 Epoch 399/1200, Train Loss: 0.2299, Train Accuracy: 0.9272, Val Loss: 0.2141, Val Accuracy: 0.9279 Epoch 400/1200, Train Loss: 0.2294, Train Accuracy: 0.9265, Val Loss: 0.2131, Val Accuracy: 0.9281 Epoch 401/1200, Train Loss: 0.2270, Train Accuracy: 0.9298, Val Loss: 0.2122, Val Accuracy: 0.9283 Epoch 402/1200, Train Loss: 0.2254, Train Accuracy: 0.9290, Val Loss: 0.2113, Val Accuracy: 0.9283 Epoch 403/1200, Train Loss: 0.2246, Train Accuracy: 0.9298, Val Loss: 0.2104, Val Accuracy: 0.9285 Epoch 404/1200, Train Loss: 0.2232, Train Accuracy: 0.9296, Val Loss: 0.2095, Val Accuracy: 0.9287 Epoch 405/1200, Train Loss: 0.2243, Train Accuracy: 0.9295, Val Loss: 0.2086, Val Accuracy: 0.9288 Epoch 406/1200, Train Loss: 0.2210, Train Accuracy: 0.9302, Val Loss: 0.2077, Val Accuracy: 0.9290 Epoch 407/1200, Train Loss: 0.2215, Train Accuracy: 0.9302, Val Loss: 0.2068, Val Accuracy: 0.9290 Epoch 408/1200, Train Loss: 0.2196, Train Accuracy: 0.9296, Val Loss: 0.2060, Val Accuracy: 0.9287 Epoch 409/1200, Train Loss: 0.2190, Train Accuracy: 0.9307, Val Loss: 0.2051, Val Accuracy: 0.9285 Epoch 410/1200, Train Loss: 0.2180, Train Accuracy: 0.9303, Val Loss: 0.2043, Val Accuracy: 0.9290 Epoch 411/1200, Train Loss: 0.2173, Train Accuracy: 0.9302, Val Loss: 0.2035, Val Accuracy: 0.9298 Epoch 412/1200, Train Loss: 0.2153, Train Accuracy: 0.9303, Val Loss: 0.2027, Val Accuracy: 0.9300 Epoch 413/1200, Train Loss: 0.2155, Train Accuracy: 0.9310, Val Loss: 0.2019, Val Accuracy: 0.9306 Epoch 414/1200, Train Loss: 0.2132, Train Accuracy: 0.9323, Val Loss: 0.2011, Val Accuracy: 0.9308 Epoch 415/1200, Train Loss: 0.2128, Train Accuracy: 0.9324, Val Loss: 0.2003, Val Accuracy: 0.9310 Epoch 416/1200, Train Loss: 0.2118, Train Accuracy: 0.9306, Val Loss: 0.1996, Val Accuracy: 0.9312 Epoch 417/1200, Train Loss: 0.2120, Train Accuracy: 0.9327, Val Loss: 0.1989, Val Accuracy: 0.9312 Epoch 418/1200, Train Loss: 0.2111, Train Accuracy: 0.9337, Val Loss: 0.1981, Val Accuracy: 0.9310 Epoch 419/1200, Train Loss: 0.2090, Train Accuracy: 0.9328, Val Loss: 0.1974, Val Accuracy: 0.9310 Epoch 420/1200, Train Loss: 0.2071, Train Accuracy: 0.9339, Val Loss: 0.1966, Val Accuracy: 0.9310 Epoch 421/1200, Train Loss: 0.2090, Train Accuracy: 0.9321, Val Loss: 0.1959, Val Accuracy: 0.9310 Epoch 422/1200, Train Loss: 0.2087, Train Accuracy: 0.9310, Val Loss: 0.1953, Val Accuracy: 0.9312 Epoch 423/1200, Train Loss: 0.2077, Train Accuracy: 0.9330, Val Loss: 0.1946, Val Accuracy: 0.9314 Epoch 424/1200, Train Loss: 0.2070, Train Accuracy: 0.9338, Val Loss: 0.1939, Val Accuracy: 0.9314 Epoch 425/1200, Train Loss: 0.2060, Train Accuracy: 0.9325, Val Loss: 0.1933, Val Accuracy: 0.9314 Epoch 426/1200, Train Loss: 0.2045, Train Accuracy: 0.9340, Val Loss: 0.1927, Val Accuracy: 0.9316 Epoch 427/1200, Train Loss: 0.2029, Train Accuracy: 0.9343, Val Loss: 0.1920, Val Accuracy: 0.9318 Epoch 428/1200, Train Loss: 0.2026, Train Accuracy: 0.9331, Val Loss: 0.1914, Val Accuracy: 0.9320 Epoch 429/1200, Train Loss: 0.2007, Train Accuracy: 0.9348, Val Loss: 0.1907, Val Accuracy: 0.9325 Epoch 430/1200, Train Loss: 0.2017, Train Accuracy: 0.9341, Val Loss: 0.1901, Val Accuracy: 0.9327 Epoch 431/1200, Train Loss: 0.2007, Train Accuracy: 0.9349, Val Loss: 0.1895, Val Accuracy: 0.9327 Epoch 432/1200, Train Loss: 0.1986, Train Accuracy: 0.9359, Val Loss: 0.1888, Val Accuracy: 0.9331 Epoch 433/1200, Train Loss: 0.1989, Train Accuracy: 0.9355, Val Loss: 0.1882, Val Accuracy: 0.9331 Epoch 434/1200, Train Loss: 0.1969, Train Accuracy: 0.9355, Val Loss: 0.1876, Val Accuracy: 0.9333 Epoch 435/1200, Train Loss: 0.1969, Train Accuracy: 0.9358, Val Loss: 0.1871, Val Accuracy: 0.9333 Epoch 436/1200, Train Loss: 0.1963, Train Accuracy: 0.9357, Val Loss: 0.1865, Val Accuracy: 0.9333 Epoch 437/1200, Train Loss: 0.1945, Train Accuracy: 0.9347, Val Loss: 0.1859, Val Accuracy: 0.9333 Epoch 438/1200, Train Loss: 0.1963, Train Accuracy: 0.9345, Val Loss: 0.1853, Val Accuracy: 0.9333 Epoch 439/1200, Train Loss: 0.1939, Train Accuracy: 0.9355, Val Loss: 0.1848, Val Accuracy: 0.9335 Epoch 440/1200, Train Loss: 0.1934, Train Accuracy: 0.9352, Val Loss: 0.1842, Val Accuracy: 0.9339 Epoch 441/1200, Train Loss: 0.1924, Train Accuracy: 0.9350, Val Loss: 0.1836, Val Accuracy: 0.9341 Epoch 442/1200, Train Loss: 0.1925, Train Accuracy: 0.9369, Val Loss: 0.1831, Val Accuracy: 0.9345 Epoch 443/1200, Train Loss: 0.1926, Train Accuracy: 0.9359, Val Loss: 0.1826, Val Accuracy: 0.9345 Epoch 444/1200, Train Loss: 0.1897, Train Accuracy: 0.9357, Val Loss: 0.1820, Val Accuracy: 0.9345 Epoch 445/1200, Train Loss: 0.1900, Train Accuracy: 0.9362, Val Loss: 0.1815, Val Accuracy: 0.9345 Epoch 446/1200, Train Loss: 0.1894, Train Accuracy: 0.9375, Val Loss: 0.1810, Val Accuracy: 0.9345 Epoch 447/1200, Train Loss: 0.1888, Train Accuracy: 0.9365, Val Loss: 0.1804, Val Accuracy: 0.9345 Epoch 448/1200, Train Loss: 0.1881, Train Accuracy: 0.9383, Val Loss: 0.1800, Val Accuracy: 0.9345 Epoch 449/1200, Train Loss: 0.1879, Train Accuracy: 0.9361, Val Loss: 0.1795, Val Accuracy: 0.9343 Epoch 450/1200, Train Loss: 0.1862, Train Accuracy: 0.9384, Val Loss: 0.1790, Val Accuracy: 0.9345 Epoch 451/1200, Train Loss: 0.1855, Train Accuracy: 0.9381, Val Loss: 0.1785, Val Accuracy: 0.9345 Epoch 452/1200, Train Loss: 0.1845, Train Accuracy: 0.9385, Val Loss: 0.1780, Val Accuracy: 0.9347 Epoch 453/1200, Train Loss: 0.1860, Train Accuracy: 0.9369, Val Loss: 0.1775, Val Accuracy: 0.9347 Epoch 454/1200, Train Loss: 0.1844, Train Accuracy: 0.9373, Val Loss: 0.1771, Val Accuracy: 0.9347 Epoch 455/1200, Train Loss: 0.1853, Train Accuracy: 0.9367, Val Loss: 0.1766, Val Accuracy: 0.9353 Epoch 456/1200, Train Loss: 0.1828, Train Accuracy: 0.9389, Val Loss: 0.1762, Val Accuracy: 0.9351 Epoch 457/1200, Train Loss: 0.1811, Train Accuracy: 0.9387, Val Loss: 0.1757, Val Accuracy: 0.9353 Epoch 458/1200, Train Loss: 0.1811, Train Accuracy: 0.9374, Val Loss: 0.1753, Val Accuracy: 0.9353 Epoch 459/1200, Train Loss: 0.1824, Train Accuracy: 0.9389, Val Loss: 0.1749, Val Accuracy: 0.9349 Epoch 460/1200, Train Loss: 0.1789, Train Accuracy: 0.9393, Val Loss: 0.1744, Val Accuracy: 0.9353 Epoch 461/1200, Train Loss: 0.1804, Train Accuracy: 0.9391, Val Loss: 0.1740, Val Accuracy: 0.9353 Epoch 462/1200, Train Loss: 0.1788, Train Accuracy: 0.9401, Val Loss: 0.1735, Val Accuracy: 0.9353 Epoch 463/1200, Train Loss: 0.1800, Train Accuracy: 0.9396, Val Loss: 0.1731, Val Accuracy: 0.9355 Epoch 464/1200, Train Loss: 0.1774, Train Accuracy: 0.9404, Val Loss: 0.1727, Val Accuracy: 0.9355 Epoch 465/1200, Train Loss: 0.1776, Train Accuracy: 0.9394, Val Loss: 0.1722, Val Accuracy: 0.9358 Epoch 466/1200, Train Loss: 0.1767, Train Accuracy: 0.9403, Val Loss: 0.1718, Val Accuracy: 0.9358 Epoch 467/1200, Train Loss: 0.1755, Train Accuracy: 0.9428, Val Loss: 0.1714, Val Accuracy: 0.9358 Epoch 468/1200, Train Loss: 0.1745, Train Accuracy: 0.9418, Val Loss: 0.1710, Val Accuracy: 0.9360 Epoch 469/1200, Train Loss: 0.1761, Train Accuracy: 0.9407, Val Loss: 0.1706, Val Accuracy: 0.9364 Epoch 470/1200, Train Loss: 0.1732, Train Accuracy: 0.9427, Val Loss: 0.1702, Val Accuracy: 0.9368 Epoch 471/1200, Train Loss: 0.1748, Train Accuracy: 0.9405, Val Loss: 0.1698, Val Accuracy: 0.9368 Epoch 472/1200, Train Loss: 0.1730, Train Accuracy: 0.9427, Val Loss: 0.1694, Val Accuracy: 0.9368 Epoch 473/1200, Train Loss: 0.1741, Train Accuracy: 0.9401, Val Loss: 0.1691, Val Accuracy: 0.9372 Epoch 474/1200, Train Loss: 0.1731, Train Accuracy: 0.9410, Val Loss: 0.1687, Val Accuracy: 0.9374 Epoch 475/1200, Train Loss: 0.1717, Train Accuracy: 0.9419, Val Loss: 0.1683, Val Accuracy: 0.9374 Epoch 476/1200, Train Loss: 0.1739, Train Accuracy: 0.9408, Val Loss: 0.1679, Val Accuracy: 0.9372 Epoch 477/1200, Train Loss: 0.1709, Train Accuracy: 0.9417, Val Loss: 0.1676, Val Accuracy: 0.9374 Epoch 478/1200, Train Loss: 0.1711, Train Accuracy: 0.9426, Val Loss: 0.1672, Val Accuracy: 0.9374 Epoch 479/1200, Train Loss: 0.1709, Train Accuracy: 0.9415, Val Loss: 0.1668, Val Accuracy: 0.9376 Epoch 480/1200, Train Loss: 0.1697, Train Accuracy: 0.9419, Val Loss: 0.1665, Val Accuracy: 0.9378 Epoch 481/1200, Train Loss: 0.1699, Train Accuracy: 0.9421, Val Loss: 0.1661, Val Accuracy: 0.9382 Epoch 482/1200, Train Loss: 0.1682, Train Accuracy: 0.9431, Val Loss: 0.1658, Val Accuracy: 0.9382 Epoch 483/1200, Train Loss: 0.1672, Train Accuracy: 0.9427, Val Loss: 0.1654, Val Accuracy: 0.9384 Epoch 484/1200, Train Loss: 0.1686, Train Accuracy: 0.9413, Val Loss: 0.1651, Val Accuracy: 0.9390 Epoch 485/1200, Train Loss: 0.1661, Train Accuracy: 0.9446, Val Loss: 0.1647, Val Accuracy: 0.9390 Epoch 486/1200, Train Loss: 0.1658, Train Accuracy: 0.9440, Val Loss: 0.1644, Val Accuracy: 0.9392 Epoch 487/1200, Train Loss: 0.1666, Train Accuracy: 0.9424, Val Loss: 0.1641, Val Accuracy: 0.9392 Epoch 488/1200, Train Loss: 0.1664, Train Accuracy: 0.9429, Val Loss: 0.1637, Val Accuracy: 0.9392 Epoch 489/1200, Train Loss: 0.1674, Train Accuracy: 0.9414, Val Loss: 0.1634, Val Accuracy: 0.9393 Epoch 490/1200, Train Loss: 0.1658, Train Accuracy: 0.9421, Val Loss: 0.1631, Val Accuracy: 0.9393 Epoch 491/1200, Train Loss: 0.1643, Train Accuracy: 0.9446, Val Loss: 0.1628, Val Accuracy: 0.9393 Epoch 492/1200, Train Loss: 0.1632, Train Accuracy: 0.9442, Val Loss: 0.1625, Val Accuracy: 0.9393 Epoch 493/1200, Train Loss: 0.1634, Train Accuracy: 0.9445, Val Loss: 0.1622, Val Accuracy: 0.9393 Epoch 494/1200, Train Loss: 0.1642, Train Accuracy: 0.9437, Val Loss: 0.1619, Val Accuracy: 0.9393 Epoch 495/1200, Train Loss: 0.1622, Train Accuracy: 0.9435, Val Loss: 0.1616, Val Accuracy: 0.9393 Epoch 496/1200, Train Loss: 0.1607, Train Accuracy: 0.9436, Val Loss: 0.1613, Val Accuracy: 0.9393 Epoch 497/1200, Train Loss: 0.1632, Train Accuracy: 0.9440, Val Loss: 0.1610, Val Accuracy: 0.9392 Epoch 498/1200, Train Loss: 0.1600, Train Accuracy: 0.9443, Val Loss: 0.1607, Val Accuracy: 0.9393 Epoch 499/1200, Train Loss: 0.1596, Train Accuracy: 0.9460, Val Loss: 0.1604, Val Accuracy: 0.9392 Epoch 500/1200, Train Loss: 0.1599, Train Accuracy: 0.9450, Val Loss: 0.1601, Val Accuracy: 0.9392 Epoch 501/1200, Train Loss: 0.1590, Train Accuracy: 0.9452, Val Loss: 0.1598, Val Accuracy: 0.9392 Epoch 502/1200, Train Loss: 0.1599, Train Accuracy: 0.9458, Val Loss: 0.1595, Val Accuracy: 0.9393 Epoch 503/1200, Train Loss: 0.1601, Train Accuracy: 0.9454, Val Loss: 0.1593, Val Accuracy: 0.9395 Epoch 504/1200, Train Loss: 0.1608, Train Accuracy: 0.9449, Val Loss: 0.1590, Val Accuracy: 0.9393 Epoch 505/1200, Train Loss: 0.1581, Train Accuracy: 0.9437, Val Loss: 0.1587, Val Accuracy: 0.9395 Epoch 506/1200, Train Loss: 0.1586, Train Accuracy: 0.9455, Val Loss: 0.1585, Val Accuracy: 0.9393 Epoch 507/1200, Train Loss: 0.1561, Train Accuracy: 0.9466, Val Loss: 0.1582, Val Accuracy: 0.9395 Epoch 508/1200, Train Loss: 0.1585, Train Accuracy: 0.9455, Val Loss: 0.1579, Val Accuracy: 0.9395 Epoch 509/1200, Train Loss: 0.1570, Train Accuracy: 0.9471, Val Loss: 0.1577, Val Accuracy: 0.9395 Epoch 510/1200, Train Loss: 0.1557, Train Accuracy: 0.9459, Val Loss: 0.1574, Val Accuracy: 0.9395 Epoch 511/1200, Train Loss: 0.1545, Train Accuracy: 0.9467, Val Loss: 0.1571, Val Accuracy: 0.9395 Epoch 512/1200, Train Loss: 0.1548, Train Accuracy: 0.9473, Val Loss: 0.1568, Val Accuracy: 0.9395 Epoch 513/1200, Train Loss: 0.1541, Train Accuracy: 0.9466, Val Loss: 0.1566, Val Accuracy: 0.9397 Epoch 514/1200, Train Loss: 0.1546, Train Accuracy: 0.9476, Val Loss: 0.1563, Val Accuracy: 0.9395 Epoch 515/1200, Train Loss: 0.1524, Train Accuracy: 0.9476, Val Loss: 0.1561, Val Accuracy: 0.9399 Epoch 516/1200, Train Loss: 0.1541, Train Accuracy: 0.9460, Val Loss: 0.1558, Val Accuracy: 0.9399 Epoch 517/1200, Train Loss: 0.1523, Train Accuracy: 0.9477, Val Loss: 0.1556, Val Accuracy: 0.9401 Epoch 518/1200, Train Loss: 0.1518, Train Accuracy: 0.9481, Val Loss: 0.1553, Val Accuracy: 0.9403 Epoch 519/1200, Train Loss: 0.1525, Train Accuracy: 0.9484, Val Loss: 0.1551, Val Accuracy: 0.9399 Epoch 520/1200, Train Loss: 0.1507, Train Accuracy: 0.9477, Val Loss: 0.1549, Val Accuracy: 0.9403 Epoch 521/1200, Train Loss: 0.1521, Train Accuracy: 0.9464, Val Loss: 0.1546, Val Accuracy: 0.9405 Epoch 522/1200, Train Loss: 0.1521, Train Accuracy: 0.9464, Val Loss: 0.1544, Val Accuracy: 0.9407 Epoch 523/1200, Train Loss: 0.1510, Train Accuracy: 0.9477, Val Loss: 0.1541, Val Accuracy: 0.9409 Epoch 524/1200, Train Loss: 0.1502, Train Accuracy: 0.9491, Val Loss: 0.1539, Val Accuracy: 0.9409 Epoch 525/1200, Train Loss: 0.1506, Train Accuracy: 0.9465, Val Loss: 0.1537, Val Accuracy: 0.9409 Epoch 526/1200, Train Loss: 0.1489, Train Accuracy: 0.9495, Val Loss: 0.1534, Val Accuracy: 0.9409 Epoch 527/1200, Train Loss: 0.1478, Train Accuracy: 0.9488, Val Loss: 0.1532, Val Accuracy: 0.9407 Epoch 528/1200, Train Loss: 0.1466, Train Accuracy: 0.9507, Val Loss: 0.1530, Val Accuracy: 0.9409 Epoch 529/1200, Train Loss: 0.1483, Train Accuracy: 0.9487, Val Loss: 0.1528, Val Accuracy: 0.9411 Epoch 530/1200, Train Loss: 0.1460, Train Accuracy: 0.9518, Val Loss: 0.1525, Val Accuracy: 0.9409 Epoch 531/1200, Train Loss: 0.1474, Train Accuracy: 0.9493, Val Loss: 0.1523, Val Accuracy: 0.9411 Epoch 532/1200, Train Loss: 0.1475, Train Accuracy: 0.9477, Val Loss: 0.1521, Val Accuracy: 0.9415 Epoch 533/1200, Train Loss: 0.1465, Train Accuracy: 0.9502, Val Loss: 0.1519, Val Accuracy: 0.9413 Epoch 534/1200, Train Loss: 0.1465, Train Accuracy: 0.9484, Val Loss: 0.1516, Val Accuracy: 0.9411 Epoch 535/1200, Train Loss: 0.1467, Train Accuracy: 0.9483, Val Loss: 0.1514, Val Accuracy: 0.9411 Epoch 536/1200, Train Loss: 0.1444, Train Accuracy: 0.9500, Val Loss: 0.1512, Val Accuracy: 0.9411 Epoch 537/1200, Train Loss: 0.1445, Train Accuracy: 0.9505, Val Loss: 0.1510, Val Accuracy: 0.9411 Epoch 538/1200, Train Loss: 0.1431, Train Accuracy: 0.9503, Val Loss: 0.1508, Val Accuracy: 0.9417 Epoch 539/1200, Train Loss: 0.1443, Train Accuracy: 0.9508, Val Loss: 0.1506, Val Accuracy: 0.9415 Epoch 540/1200, Train Loss: 0.1433, Train Accuracy: 0.9502, Val Loss: 0.1504, Val Accuracy: 0.9415 Epoch 541/1200, Train Loss: 0.1438, Train Accuracy: 0.9500, Val Loss: 0.1502, Val Accuracy: 0.9415 Epoch 542/1200, Train Loss: 0.1431, Train Accuracy: 0.9504, Val Loss: 0.1500, Val Accuracy: 0.9415 Epoch 543/1200, Train Loss: 0.1411, Train Accuracy: 0.9512, Val Loss: 0.1498, Val Accuracy: 0.9417 Epoch 544/1200, Train Loss: 0.1419, Train Accuracy: 0.9506, Val Loss: 0.1496, Val Accuracy: 0.9417 Epoch 545/1200, Train Loss: 0.1428, Train Accuracy: 0.9500, Val Loss: 0.1494, Val Accuracy: 0.9417 Epoch 546/1200, Train Loss: 0.1397, Train Accuracy: 0.9514, Val Loss: 0.1492, Val Accuracy: 0.9417 Epoch 547/1200, Train Loss: 0.1426, Train Accuracy: 0.9495, Val Loss: 0.1490, Val Accuracy: 0.9417 Epoch 548/1200, Train Loss: 0.1416, Train Accuracy: 0.9516, Val Loss: 0.1488, Val Accuracy: 0.9417 Epoch 549/1200, Train Loss: 0.1405, Train Accuracy: 0.9512, Val Loss: 0.1486, Val Accuracy: 0.9417 Epoch 550/1200, Train Loss: 0.1404, Train Accuracy: 0.9519, Val Loss: 0.1485, Val Accuracy: 0.9417 Epoch 551/1200, Train Loss: 0.1389, Train Accuracy: 0.9523, Val Loss: 0.1483, Val Accuracy: 0.9419 Epoch 552/1200, Train Loss: 0.1393, Train Accuracy: 0.9518, Val Loss: 0.1481, Val Accuracy: 0.9419 Epoch 553/1200, Train Loss: 0.1398, Train Accuracy: 0.9521, Val Loss: 0.1479, Val Accuracy: 0.9417 Epoch 554/1200, Train Loss: 0.1394, Train Accuracy: 0.9519, Val Loss: 0.1477, Val Accuracy: 0.9421 Epoch 555/1200, Train Loss: 0.1393, Train Accuracy: 0.9512, Val Loss: 0.1476, Val Accuracy: 0.9421 Epoch 556/1200, Train Loss: 0.1386, Train Accuracy: 0.9512, Val Loss: 0.1474, Val Accuracy: 0.9421 Epoch 557/1200, Train Loss: 0.1374, Train Accuracy: 0.9533, Val Loss: 0.1472, Val Accuracy: 0.9423 Epoch 558/1200, Train Loss: 0.1382, Train Accuracy: 0.9515, Val Loss: 0.1470, Val Accuracy: 0.9423 Epoch 559/1200, Train Loss: 0.1375, Train Accuracy: 0.9531, Val Loss: 0.1469, Val Accuracy: 0.9425 Epoch 560/1200, Train Loss: 0.1398, Train Accuracy: 0.9527, Val Loss: 0.1467, Val Accuracy: 0.9425 Epoch 561/1200, Train Loss: 0.1372, Train Accuracy: 0.9516, Val Loss: 0.1465, Val Accuracy: 0.9421 Epoch 562/1200, Train Loss: 0.1358, Train Accuracy: 0.9526, Val Loss: 0.1464, Val Accuracy: 0.9421 Epoch 563/1200, Train Loss: 0.1379, Train Accuracy: 0.9523, Val Loss: 0.1462, Val Accuracy: 0.9423 Epoch 564/1200, Train Loss: 0.1344, Train Accuracy: 0.9545, Val Loss: 0.1460, Val Accuracy: 0.9419 Epoch 565/1200, Train Loss: 0.1360, Train Accuracy: 0.9528, Val Loss: 0.1458, Val Accuracy: 0.9423 Epoch 566/1200, Train Loss: 0.1355, Train Accuracy: 0.9529, Val Loss: 0.1457, Val Accuracy: 0.9423 Epoch 567/1200, Train Loss: 0.1338, Train Accuracy: 0.9536, Val Loss: 0.1455, Val Accuracy: 0.9423 Epoch 568/1200, Train Loss: 0.1345, Train Accuracy: 0.9532, Val Loss: 0.1453, Val Accuracy: 0.9423 Epoch 569/1200, Train Loss: 0.1348, Train Accuracy: 0.9519, Val Loss: 0.1452, Val Accuracy: 0.9421 Epoch 570/1200, Train Loss: 0.1324, Train Accuracy: 0.9531, Val Loss: 0.1450, Val Accuracy: 0.9423 Epoch 571/1200, Train Loss: 0.1354, Train Accuracy: 0.9552, Val Loss: 0.1448, Val Accuracy: 0.9425 Epoch 572/1200, Train Loss: 0.1330, Train Accuracy: 0.9534, Val Loss: 0.1447, Val Accuracy: 0.9423 Epoch 573/1200, Train Loss: 0.1329, Train Accuracy: 0.9534, Val Loss: 0.1445, Val Accuracy: 0.9423 Epoch 574/1200, Train Loss: 0.1327, Train Accuracy: 0.9547, Val Loss: 0.1444, Val Accuracy: 0.9421 Epoch 575/1200, Train Loss: 0.1333, Train Accuracy: 0.9530, Val Loss: 0.1442, Val Accuracy: 0.9423 Epoch 576/1200, Train Loss: 0.1323, Train Accuracy: 0.9547, Val Loss: 0.1440, Val Accuracy: 0.9421 Epoch 577/1200, Train Loss: 0.1316, Train Accuracy: 0.9553, Val Loss: 0.1438, Val Accuracy: 0.9421 Epoch 578/1200, Train Loss: 0.1315, Train Accuracy: 0.9537, Val Loss: 0.1437, Val Accuracy: 0.9423 Epoch 579/1200, Train Loss: 0.1310, Train Accuracy: 0.9555, Val Loss: 0.1435, Val Accuracy: 0.9417 Epoch 580/1200, Train Loss: 0.1310, Train Accuracy: 0.9550, Val Loss: 0.1434, Val Accuracy: 0.9417 Epoch 581/1200, Train Loss: 0.1301, Train Accuracy: 0.9553, Val Loss: 0.1432, Val Accuracy: 0.9423 Epoch 582/1200, Train Loss: 0.1290, Train Accuracy: 0.9545, Val Loss: 0.1431, Val Accuracy: 0.9423 Epoch 583/1200, Train Loss: 0.1291, Train Accuracy: 0.9564, Val Loss: 0.1429, Val Accuracy: 0.9423 Epoch 584/1200, Train Loss: 0.1286, Train Accuracy: 0.9543, Val Loss: 0.1428, Val Accuracy: 0.9423 Epoch 585/1200, Train Loss: 0.1309, Train Accuracy: 0.9542, Val Loss: 0.1426, Val Accuracy: 0.9421 Epoch 586/1200, Train Loss: 0.1307, Train Accuracy: 0.9554, Val Loss: 0.1425, Val Accuracy: 0.9421 Epoch 587/1200, Train Loss: 0.1298, Train Accuracy: 0.9555, Val Loss: 0.1424, Val Accuracy: 0.9421 Epoch 588/1200, Train Loss: 0.1278, Train Accuracy: 0.9558, Val Loss: 0.1422, Val Accuracy: 0.9421 Epoch 589/1200, Train Loss: 0.1291, Train Accuracy: 0.9563, Val Loss: 0.1421, Val Accuracy: 0.9423 Epoch 590/1200, Train Loss: 0.1293, Train Accuracy: 0.9539, Val Loss: 0.1420, Val Accuracy: 0.9425 Epoch 591/1200, Train Loss: 0.1280, Train Accuracy: 0.9561, Val Loss: 0.1418, Val Accuracy: 0.9423 Epoch 592/1200, Train Loss: 0.1272, Train Accuracy: 0.9565, Val Loss: 0.1417, Val Accuracy: 0.9423 Epoch 593/1200, Train Loss: 0.1268, Train Accuracy: 0.9558, Val Loss: 0.1416, Val Accuracy: 0.9427 Epoch 594/1200, Train Loss: 0.1268, Train Accuracy: 0.9555, Val Loss: 0.1415, Val Accuracy: 0.9428 Epoch 595/1200, Train Loss: 0.1269, Train Accuracy: 0.9557, Val Loss: 0.1414, Val Accuracy: 0.9428 Epoch 596/1200, Train Loss: 0.1260, Train Accuracy: 0.9559, Val Loss: 0.1412, Val Accuracy: 0.9430 Epoch 597/1200, Train Loss: 0.1265, Train Accuracy: 0.9560, Val Loss: 0.1411, Val Accuracy: 0.9427 Epoch 598/1200, Train Loss: 0.1262, Train Accuracy: 0.9556, Val Loss: 0.1409, Val Accuracy: 0.9430 Epoch 599/1200, Train Loss: 0.1268, Train Accuracy: 0.9574, Val Loss: 0.1408, Val Accuracy: 0.9432 Epoch 600/1200, Train Loss: 0.1249, Train Accuracy: 0.9571, Val Loss: 0.1407, Val Accuracy: 0.9436 Epoch 601/1200, Train Loss: 0.1245, Train Accuracy: 0.9576, Val Loss: 0.1406, Val Accuracy: 0.9436 Epoch 602/1200, Train Loss: 0.1244, Train Accuracy: 0.9566, Val Loss: 0.1404, Val Accuracy: 0.9434 Epoch 603/1200, Train Loss: 0.1245, Train Accuracy: 0.9568, Val Loss: 0.1404, Val Accuracy: 0.9442 Epoch 604/1200, Train Loss: 0.1235, Train Accuracy: 0.9578, Val Loss: 0.1402, Val Accuracy: 0.9444 Epoch 605/1200, Train Loss: 0.1243, Train Accuracy: 0.9573, Val Loss: 0.1401, Val Accuracy: 0.9442 Epoch 606/1200, Train Loss: 0.1228, Train Accuracy: 0.9572, Val Loss: 0.1399, Val Accuracy: 0.9442 Epoch 607/1200, Train Loss: 0.1240, Train Accuracy: 0.9562, Val Loss: 0.1398, Val Accuracy: 0.9442 Epoch 608/1200, Train Loss: 0.1228, Train Accuracy: 0.9573, Val Loss: 0.1397, Val Accuracy: 0.9442 Epoch 609/1200, Train Loss: 0.1218, Train Accuracy: 0.9587, Val Loss: 0.1396, Val Accuracy: 0.9444 Epoch 610/1200, Train Loss: 0.1225, Train Accuracy: 0.9579, Val Loss: 0.1395, Val Accuracy: 0.9448 Epoch 611/1200, Train Loss: 0.1220, Train Accuracy: 0.9580, Val Loss: 0.1394, Val Accuracy: 0.9448 Epoch 612/1200, Train Loss: 0.1232, Train Accuracy: 0.9566, Val Loss: 0.1393, Val Accuracy: 0.9448 Epoch 613/1200, Train Loss: 0.1207, Train Accuracy: 0.9587, Val Loss: 0.1391, Val Accuracy: 0.9448 Epoch 614/1200, Train Loss: 0.1194, Train Accuracy: 0.9589, Val Loss: 0.1390, Val Accuracy: 0.9448 Epoch 615/1200, Train Loss: 0.1210, Train Accuracy: 0.9591, Val Loss: 0.1389, Val Accuracy: 0.9452 Epoch 616/1200, Train Loss: 0.1199, Train Accuracy: 0.9593, Val Loss: 0.1388, Val Accuracy: 0.9452 Epoch 617/1200, Train Loss: 0.1200, Train Accuracy: 0.9591, Val Loss: 0.1387, Val Accuracy: 0.9452 Epoch 618/1200, Train Loss: 0.1202, Train Accuracy: 0.9588, Val Loss: 0.1386, Val Accuracy: 0.9456 Epoch 619/1200, Train Loss: 0.1188, Train Accuracy: 0.9586, Val Loss: 0.1384, Val Accuracy: 0.9456 Epoch 620/1200, Train Loss: 0.1171, Train Accuracy: 0.9593, Val Loss: 0.1383, Val Accuracy: 0.9452 Epoch 621/1200, Train Loss: 0.1220, Train Accuracy: 0.9580, Val Loss: 0.1381, Val Accuracy: 0.9456 Epoch 622/1200, Train Loss: 0.1187, Train Accuracy: 0.9595, Val Loss: 0.1380, Val Accuracy: 0.9458 Epoch 623/1200, Train Loss: 0.1180, Train Accuracy: 0.9594, Val Loss: 0.1379, Val Accuracy: 0.9458 Epoch 624/1200, Train Loss: 0.1190, Train Accuracy: 0.9590, Val Loss: 0.1378, Val Accuracy: 0.9458 Epoch 625/1200, Train Loss: 0.1181, Train Accuracy: 0.9591, Val Loss: 0.1377, Val Accuracy: 0.9458 Epoch 626/1200, Train Loss: 0.1175, Train Accuracy: 0.9602, Val Loss: 0.1376, Val Accuracy: 0.9462 Epoch 627/1200, Train Loss: 0.1183, Train Accuracy: 0.9588, Val Loss: 0.1375, Val Accuracy: 0.9463 Epoch 628/1200, Train Loss: 0.1183, Train Accuracy: 0.9598, Val Loss: 0.1374, Val Accuracy: 0.9462 Epoch 629/1200, Train Loss: 0.1177, Train Accuracy: 0.9598, Val Loss: 0.1373, Val Accuracy: 0.9462 Epoch 630/1200, Train Loss: 0.1174, Train Accuracy: 0.9596, Val Loss: 0.1372, Val Accuracy: 0.9462 Epoch 631/1200, Train Loss: 0.1166, Train Accuracy: 0.9587, Val Loss: 0.1371, Val Accuracy: 0.9462 Epoch 632/1200, Train Loss: 0.1171, Train Accuracy: 0.9602, Val Loss: 0.1370, Val Accuracy: 0.9463 Epoch 633/1200, Train Loss: 0.1164, Train Accuracy: 0.9609, Val Loss: 0.1369, Val Accuracy: 0.9465 Epoch 634/1200, Train Loss: 0.1152, Train Accuracy: 0.9594, Val Loss: 0.1369, Val Accuracy: 0.9465 Epoch 635/1200, Train Loss: 0.1155, Train Accuracy: 0.9607, Val Loss: 0.1368, Val Accuracy: 0.9465 Epoch 636/1200, Train Loss: 0.1135, Train Accuracy: 0.9615, Val Loss: 0.1366, Val Accuracy: 0.9467 Epoch 637/1200, Train Loss: 0.1138, Train Accuracy: 0.9618, Val Loss: 0.1365, Val Accuracy: 0.9465 Epoch 638/1200, Train Loss: 0.1145, Train Accuracy: 0.9611, Val Loss: 0.1364, Val Accuracy: 0.9465 Epoch 639/1200, Train Loss: 0.1160, Train Accuracy: 0.9586, Val Loss: 0.1363, Val Accuracy: 0.9465 Epoch 640/1200, Train Loss: 0.1139, Train Accuracy: 0.9620, Val Loss: 0.1363, Val Accuracy: 0.9463 Epoch 641/1200, Train Loss: 0.1128, Train Accuracy: 0.9609, Val Loss: 0.1361, Val Accuracy: 0.9467 Epoch 642/1200, Train Loss: 0.1133, Train Accuracy: 0.9610, Val Loss: 0.1360, Val Accuracy: 0.9469 Epoch 643/1200, Train Loss: 0.1128, Train Accuracy: 0.9614, Val Loss: 0.1359, Val Accuracy: 0.9471 Epoch 644/1200, Train Loss: 0.1138, Train Accuracy: 0.9610, Val Loss: 0.1358, Val Accuracy: 0.9467 Epoch 645/1200, Train Loss: 0.1128, Train Accuracy: 0.9609, Val Loss: 0.1358, Val Accuracy: 0.9473 Epoch 646/1200, Train Loss: 0.1119, Train Accuracy: 0.9623, Val Loss: 0.1357, Val Accuracy: 0.9471 Epoch 647/1200, Train Loss: 0.1134, Train Accuracy: 0.9605, Val Loss: 0.1357, Val Accuracy: 0.9471 Epoch 648/1200, Train Loss: 0.1130, Train Accuracy: 0.9615, Val Loss: 0.1356, Val Accuracy: 0.9473 Epoch 649/1200, Train Loss: 0.1118, Train Accuracy: 0.9617, Val Loss: 0.1355, Val Accuracy: 0.9473 Epoch 650/1200, Train Loss: 0.1124, Train Accuracy: 0.9609, Val Loss: 0.1353, Val Accuracy: 0.9473 Epoch 651/1200, Train Loss: 0.1108, Train Accuracy: 0.9627, Val Loss: 0.1352, Val Accuracy: 0.9473 Epoch 652/1200, Train Loss: 0.1123, Train Accuracy: 0.9620, Val Loss: 0.1351, Val Accuracy: 0.9471 Epoch 653/1200, Train Loss: 0.1112, Train Accuracy: 0.9628, Val Loss: 0.1350, Val Accuracy: 0.9469 Epoch 654/1200, Train Loss: 0.1112, Train Accuracy: 0.9612, Val Loss: 0.1349, Val Accuracy: 0.9467 Epoch 655/1200, Train Loss: 0.1122, Train Accuracy: 0.9618, Val Loss: 0.1348, Val Accuracy: 0.9467 Epoch 656/1200, Train Loss: 0.1101, Train Accuracy: 0.9616, Val Loss: 0.1347, Val Accuracy: 0.9469 Epoch 657/1200, Train Loss: 0.1083, Train Accuracy: 0.9624, Val Loss: 0.1346, Val Accuracy: 0.9469 Epoch 658/1200, Train Loss: 0.1081, Train Accuracy: 0.9649, Val Loss: 0.1346, Val Accuracy: 0.9475 Epoch 659/1200, Train Loss: 0.1091, Train Accuracy: 0.9631, Val Loss: 0.1345, Val Accuracy: 0.9477 Epoch 660/1200, Train Loss: 0.1093, Train Accuracy: 0.9623, Val Loss: 0.1344, Val Accuracy: 0.9475 Epoch 661/1200, Train Loss: 0.1089, Train Accuracy: 0.9629, Val Loss: 0.1344, Val Accuracy: 0.9479 Epoch 662/1200, Train Loss: 0.1099, Train Accuracy: 0.9628, Val Loss: 0.1343, Val Accuracy: 0.9477 Epoch 663/1200, Train Loss: 0.1054, Train Accuracy: 0.9641, Val Loss: 0.1342, Val Accuracy: 0.9477 Epoch 664/1200, Train Loss: 0.1082, Train Accuracy: 0.9617, Val Loss: 0.1340, Val Accuracy: 0.9475 Epoch 665/1200, Train Loss: 0.1069, Train Accuracy: 0.9623, Val Loss: 0.1339, Val Accuracy: 0.9477 Epoch 666/1200, Train Loss: 0.1101, Train Accuracy: 0.9634, Val Loss: 0.1338, Val Accuracy: 0.9479 Epoch 667/1200, Train Loss: 0.1076, Train Accuracy: 0.9625, Val Loss: 0.1338, Val Accuracy: 0.9481 Epoch 668/1200, Train Loss: 0.1090, Train Accuracy: 0.9616, Val Loss: 0.1337, Val Accuracy: 0.9477 Epoch 669/1200, Train Loss: 0.1067, Train Accuracy: 0.9638, Val Loss: 0.1337, Val Accuracy: 0.9475 Epoch 670/1200, Train Loss: 0.1064, Train Accuracy: 0.9646, Val Loss: 0.1335, Val Accuracy: 0.9483 Epoch 671/1200, Train Loss: 0.1068, Train Accuracy: 0.9631, Val Loss: 0.1334, Val Accuracy: 0.9483 Epoch 672/1200, Train Loss: 0.1060, Train Accuracy: 0.9637, Val Loss: 0.1334, Val Accuracy: 0.9483 Epoch 673/1200, Train Loss: 0.1074, Train Accuracy: 0.9637, Val Loss: 0.1334, Val Accuracy: 0.9483 Epoch 674/1200, Train Loss: 0.1050, Train Accuracy: 0.9644, Val Loss: 0.1333, Val Accuracy: 0.9483 Epoch 675/1200, Train Loss: 0.1052, Train Accuracy: 0.9654, Val Loss: 0.1332, Val Accuracy: 0.9485 Epoch 676/1200, Train Loss: 0.1043, Train Accuracy: 0.9634, Val Loss: 0.1332, Val Accuracy: 0.9483 Epoch 677/1200, Train Loss: 0.1071, Train Accuracy: 0.9626, Val Loss: 0.1332, Val Accuracy: 0.9479 Epoch 678/1200, Train Loss: 0.1072, Train Accuracy: 0.9644, Val Loss: 0.1331, Val Accuracy: 0.9481 Epoch 679/1200, Train Loss: 0.1052, Train Accuracy: 0.9638, Val Loss: 0.1330, Val Accuracy: 0.9481 Epoch 680/1200, Train Loss: 0.1052, Train Accuracy: 0.9652, Val Loss: 0.1329, Val Accuracy: 0.9479 Epoch 681/1200, Train Loss: 0.1046, Train Accuracy: 0.9642, Val Loss: 0.1328, Val Accuracy: 0.9485 Epoch 682/1200, Train Loss: 0.1051, Train Accuracy: 0.9639, Val Loss: 0.1327, Val Accuracy: 0.9485 Epoch 683/1200, Train Loss: 0.1024, Train Accuracy: 0.9653, Val Loss: 0.1326, Val Accuracy: 0.9485 Epoch 684/1200, Train Loss: 0.1035, Train Accuracy: 0.9653, Val Loss: 0.1326, Val Accuracy: 0.9483 Epoch 685/1200, Train Loss: 0.1023, Train Accuracy: 0.9650, Val Loss: 0.1325, Val Accuracy: 0.9481 Epoch 686/1200, Train Loss: 0.1058, Train Accuracy: 0.9646, Val Loss: 0.1325, Val Accuracy: 0.9483 Epoch 687/1200, Train Loss: 0.1040, Train Accuracy: 0.9650, Val Loss: 0.1324, Val Accuracy: 0.9479 Epoch 688/1200, Train Loss: 0.1036, Train Accuracy: 0.9652, Val Loss: 0.1323, Val Accuracy: 0.9483 Epoch 689/1200, Train Loss: 0.1021, Train Accuracy: 0.9662, Val Loss: 0.1322, Val Accuracy: 0.9483 Epoch 690/1200, Train Loss: 0.1024, Train Accuracy: 0.9661, Val Loss: 0.1322, Val Accuracy: 0.9483 Epoch 691/1200, Train Loss: 0.1030, Train Accuracy: 0.9657, Val Loss: 0.1321, Val Accuracy: 0.9483 Epoch 692/1200, Train Loss: 0.1024, Train Accuracy: 0.9656, Val Loss: 0.1321, Val Accuracy: 0.9483 Epoch 693/1200, Train Loss: 0.1013, Train Accuracy: 0.9659, Val Loss: 0.1320, Val Accuracy: 0.9483 Epoch 694/1200, Train Loss: 0.1025, Train Accuracy: 0.9660, Val Loss: 0.1320, Val Accuracy: 0.9479 Epoch 695/1200, Train Loss: 0.1020, Train Accuracy: 0.9659, Val Loss: 0.1319, Val Accuracy: 0.9477 Epoch 696/1200, Train Loss: 0.0986, Train Accuracy: 0.9665, Val Loss: 0.1318, Val Accuracy: 0.9481 Epoch 697/1200, Train Loss: 0.1011, Train Accuracy: 0.9662, Val Loss: 0.1318, Val Accuracy: 0.9481 Epoch 698/1200, Train Loss: 0.1014, Train Accuracy: 0.9647, Val Loss: 0.1317, Val Accuracy: 0.9477 Epoch 699/1200, Train Loss: 0.1012, Train Accuracy: 0.9663, Val Loss: 0.1316, Val Accuracy: 0.9483 Epoch 700/1200, Train Loss: 0.0997, Train Accuracy: 0.9670, Val Loss: 0.1316, Val Accuracy: 0.9481 Epoch 701/1200, Train Loss: 0.1007, Train Accuracy: 0.9676, Val Loss: 0.1315, Val Accuracy: 0.9481 Epoch 702/1200, Train Loss: 0.1009, Train Accuracy: 0.9663, Val Loss: 0.1314, Val Accuracy: 0.9479 Epoch 703/1200, Train Loss: 0.0994, Train Accuracy: 0.9667, Val Loss: 0.1313, Val Accuracy: 0.9477 Epoch 704/1200, Train Loss: 0.1002, Train Accuracy: 0.9664, Val Loss: 0.1313, Val Accuracy: 0.9479 Epoch 705/1200, Train Loss: 0.1004, Train Accuracy: 0.9658, Val Loss: 0.1312, Val Accuracy: 0.9481 Epoch 706/1200, Train Loss: 0.0979, Train Accuracy: 0.9674, Val Loss: 0.1311, Val Accuracy: 0.9481 Epoch 707/1200, Train Loss: 0.0988, Train Accuracy: 0.9666, Val Loss: 0.1310, Val Accuracy: 0.9485 Epoch 708/1200, Train Loss: 0.0993, Train Accuracy: 0.9669, Val Loss: 0.1310, Val Accuracy: 0.9483 Epoch 709/1200, Train Loss: 0.0984, Train Accuracy: 0.9673, Val Loss: 0.1309, Val Accuracy: 0.9483 Epoch 710/1200, Train Loss: 0.0974, Train Accuracy: 0.9686, Val Loss: 0.1309, Val Accuracy: 0.9485 Epoch 711/1200, Train Loss: 0.1002, Train Accuracy: 0.9663, Val Loss: 0.1309, Val Accuracy: 0.9481 Epoch 712/1200, Train Loss: 0.0985, Train Accuracy: 0.9673, Val Loss: 0.1308, Val Accuracy: 0.9483 Epoch 713/1200, Train Loss: 0.0965, Train Accuracy: 0.9686, Val Loss: 0.1307, Val Accuracy: 0.9483 Epoch 714/1200, Train Loss: 0.0964, Train Accuracy: 0.9686, Val Loss: 0.1306, Val Accuracy: 0.9481 Epoch 715/1200, Train Loss: 0.0970, Train Accuracy: 0.9675, Val Loss: 0.1306, Val Accuracy: 0.9481 Epoch 716/1200, Train Loss: 0.0968, Train Accuracy: 0.9672, Val Loss: 0.1305, Val Accuracy: 0.9483 Epoch 717/1200, Train Loss: 0.0978, Train Accuracy: 0.9664, Val Loss: 0.1304, Val Accuracy: 0.9483 Epoch 718/1200, Train Loss: 0.0976, Train Accuracy: 0.9678, Val Loss: 0.1304, Val Accuracy: 0.9483 Epoch 719/1200, Train Loss: 0.0951, Train Accuracy: 0.9694, Val Loss: 0.1304, Val Accuracy: 0.9483 Epoch 720/1200, Train Loss: 0.0971, Train Accuracy: 0.9666, Val Loss: 0.1303, Val Accuracy: 0.9483 Epoch 721/1200, Train Loss: 0.0965, Train Accuracy: 0.9670, Val Loss: 0.1303, Val Accuracy: 0.9483 Epoch 722/1200, Train Loss: 0.0944, Train Accuracy: 0.9691, Val Loss: 0.1303, Val Accuracy: 0.9483 Epoch 723/1200, Train Loss: 0.0961, Train Accuracy: 0.9677, Val Loss: 0.1302, Val Accuracy: 0.9483 Epoch 724/1200, Train Loss: 0.0959, Train Accuracy: 0.9669, Val Loss: 0.1302, Val Accuracy: 0.9481 Epoch 725/1200, Train Loss: 0.0942, Train Accuracy: 0.9683, Val Loss: 0.1301, Val Accuracy: 0.9481 Epoch 726/1200, Train Loss: 0.0942, Train Accuracy: 0.9686, Val Loss: 0.1300, Val Accuracy: 0.9481 Epoch 727/1200, Train Loss: 0.0972, Train Accuracy: 0.9674, Val Loss: 0.1300, Val Accuracy: 0.9479 Epoch 728/1200, Train Loss: 0.0954, Train Accuracy: 0.9687, Val Loss: 0.1299, Val Accuracy: 0.9479 Epoch 729/1200, Train Loss: 0.0929, Train Accuracy: 0.9699, Val Loss: 0.1299, Val Accuracy: 0.9481 Epoch 730/1200, Train Loss: 0.0952, Train Accuracy: 0.9680, Val Loss: 0.1298, Val Accuracy: 0.9483 Epoch 731/1200, Train Loss: 0.0935, Train Accuracy: 0.9687, Val Loss: 0.1298, Val Accuracy: 0.9481 Epoch 732/1200, Train Loss: 0.0942, Train Accuracy: 0.9684, Val Loss: 0.1298, Val Accuracy: 0.9477 Epoch 733/1200, Train Loss: 0.0931, Train Accuracy: 0.9680, Val Loss: 0.1297, Val Accuracy: 0.9477 Epoch 734/1200, Train Loss: 0.0918, Train Accuracy: 0.9697, Val Loss: 0.1296, Val Accuracy: 0.9481 Epoch 735/1200, Train Loss: 0.0936, Train Accuracy: 0.9700, Val Loss: 0.1295, Val Accuracy: 0.9479 Epoch 736/1200, Train Loss: 0.0934, Train Accuracy: 0.9695, Val Loss: 0.1294, Val Accuracy: 0.9479 Epoch 737/1200, Train Loss: 0.0924, Train Accuracy: 0.9685, Val Loss: 0.1294, Val Accuracy: 0.9479 Epoch 738/1200, Train Loss: 0.0915, Train Accuracy: 0.9689, Val Loss: 0.1294, Val Accuracy: 0.9479 Epoch 739/1200, Train Loss: 0.0918, Train Accuracy: 0.9700, Val Loss: 0.1293, Val Accuracy: 0.9479 Epoch 740/1200, Train Loss: 0.0936, Train Accuracy: 0.9686, Val Loss: 0.1292, Val Accuracy: 0.9479 Epoch 741/1200, Train Loss: 0.0935, Train Accuracy: 0.9690, Val Loss: 0.1291, Val Accuracy: 0.9477 Epoch 742/1200, Train Loss: 0.0916, Train Accuracy: 0.9690, Val Loss: 0.1291, Val Accuracy: 0.9477 Epoch 743/1200, Train Loss: 0.0920, Train Accuracy: 0.9701, Val Loss: 0.1291, Val Accuracy: 0.9481 Epoch 744/1200, Train Loss: 0.0908, Train Accuracy: 0.9703, Val Loss: 0.1290, Val Accuracy: 0.9481 Epoch 745/1200, Train Loss: 0.0907, Train Accuracy: 0.9709, Val Loss: 0.1290, Val Accuracy: 0.9479 Epoch 746/1200, Train Loss: 0.0893, Train Accuracy: 0.9713, Val Loss: 0.1289, Val Accuracy: 0.9481 Epoch 747/1200, Train Loss: 0.0903, Train Accuracy: 0.9702, Val Loss: 0.1289, Val Accuracy: 0.9481 Epoch 748/1200, Train Loss: 0.0897, Train Accuracy: 0.9712, Val Loss: 0.1289, Val Accuracy: 0.9481 Epoch 749/1200, Train Loss: 0.0917, Train Accuracy: 0.9690, Val Loss: 0.1288, Val Accuracy: 0.9481 Epoch 750/1200, Train Loss: 0.0929, Train Accuracy: 0.9699, Val Loss: 0.1288, Val Accuracy: 0.9479 Epoch 751/1200, Train Loss: 0.0912, Train Accuracy: 0.9693, Val Loss: 0.1287, Val Accuracy: 0.9479 Epoch 752/1200, Train Loss: 0.0897, Train Accuracy: 0.9709, Val Loss: 0.1287, Val Accuracy: 0.9479 Epoch 753/1200, Train Loss: 0.0883, Train Accuracy: 0.9723, Val Loss: 0.1287, Val Accuracy: 0.9479 Epoch 754/1200, Train Loss: 0.0894, Train Accuracy: 0.9704, Val Loss: 0.1286, Val Accuracy: 0.9481 Epoch 755/1200, Train Loss: 0.0889, Train Accuracy: 0.9709, Val Loss: 0.1286, Val Accuracy: 0.9479 Epoch 756/1200, Train Loss: 0.0893, Train Accuracy: 0.9704, Val Loss: 0.1286, Val Accuracy: 0.9481 Epoch 757/1200, Train Loss: 0.0886, Train Accuracy: 0.9697, Val Loss: 0.1286, Val Accuracy: 0.9479 Epoch 758/1200, Train Loss: 0.0876, Train Accuracy: 0.9713, Val Loss: 0.1286, Val Accuracy: 0.9481 Epoch 759/1200, Train Loss: 0.0901, Train Accuracy: 0.9710, Val Loss: 0.1286, Val Accuracy: 0.9481 Epoch 760/1200, Train Loss: 0.0872, Train Accuracy: 0.9721, Val Loss: 0.1285, Val Accuracy: 0.9479 Epoch 761/1200, Train Loss: 0.0876, Train Accuracy: 0.9715, Val Loss: 0.1284, Val Accuracy: 0.9481 Epoch 762/1200, Train Loss: 0.0875, Train Accuracy: 0.9716, Val Loss: 0.1284, Val Accuracy: 0.9481 Epoch 763/1200, Train Loss: 0.0869, Train Accuracy: 0.9714, Val Loss: 0.1283, Val Accuracy: 0.9479 Epoch 764/1200, Train Loss: 0.0866, Train Accuracy: 0.9712, Val Loss: 0.1283, Val Accuracy: 0.9481 Epoch 765/1200, Train Loss: 0.0895, Train Accuracy: 0.9705, Val Loss: 0.1282, Val Accuracy: 0.9479 Epoch 766/1200, Train Loss: 0.0864, Train Accuracy: 0.9719, Val Loss: 0.1282, Val Accuracy: 0.9481 Epoch 767/1200, Train Loss: 0.0885, Train Accuracy: 0.9698, Val Loss: 0.1281, Val Accuracy: 0.9477 Epoch 768/1200, Train Loss: 0.0865, Train Accuracy: 0.9721, Val Loss: 0.1281, Val Accuracy: 0.9479 Epoch 769/1200, Train Loss: 0.0857, Train Accuracy: 0.9724, Val Loss: 0.1281, Val Accuracy: 0.9479 Epoch 770/1200, Train Loss: 0.0858, Train Accuracy: 0.9708, Val Loss: 0.1281, Val Accuracy: 0.9481 Epoch 771/1200, Train Loss: 0.0866, Train Accuracy: 0.9718, Val Loss: 0.1281, Val Accuracy: 0.9483 Epoch 772/1200, Train Loss: 0.0863, Train Accuracy: 0.9716, Val Loss: 0.1279, Val Accuracy: 0.9481 Epoch 773/1200, Train Loss: 0.0851, Train Accuracy: 0.9720, Val Loss: 0.1279, Val Accuracy: 0.9481 Epoch 774/1200, Train Loss: 0.0835, Train Accuracy: 0.9736, Val Loss: 0.1278, Val Accuracy: 0.9479 Epoch 775/1200, Train Loss: 0.0857, Train Accuracy: 0.9714, Val Loss: 0.1279, Val Accuracy: 0.9477 Epoch 776/1200, Train Loss: 0.0850, Train Accuracy: 0.9727, Val Loss: 0.1279, Val Accuracy: 0.9479 Epoch 777/1200, Train Loss: 0.0849, Train Accuracy: 0.9725, Val Loss: 0.1278, Val Accuracy: 0.9481 Epoch 778/1200, Train Loss: 0.0848, Train Accuracy: 0.9715, Val Loss: 0.1277, Val Accuracy: 0.9483 Epoch 779/1200, Train Loss: 0.0849, Train Accuracy: 0.9723, Val Loss: 0.1277, Val Accuracy: 0.9483 Epoch 780/1200, Train Loss: 0.0846, Train Accuracy: 0.9734, Val Loss: 0.1276, Val Accuracy: 0.9481 Epoch 781/1200, Train Loss: 0.0867, Train Accuracy: 0.9713, Val Loss: 0.1277, Val Accuracy: 0.9481 Epoch 782/1200, Train Loss: 0.0834, Train Accuracy: 0.9729, Val Loss: 0.1277, Val Accuracy: 0.9487 Epoch 783/1200, Train Loss: 0.0827, Train Accuracy: 0.9729, Val Loss: 0.1277, Val Accuracy: 0.9487 Epoch 784/1200, Train Loss: 0.0825, Train Accuracy: 0.9733, Val Loss: 0.1277, Val Accuracy: 0.9487 Epoch 785/1200, Train Loss: 0.0841, Train Accuracy: 0.9726, Val Loss: 0.1277, Val Accuracy: 0.9487 Epoch 786/1200, Train Loss: 0.0824, Train Accuracy: 0.9737, Val Loss: 0.1277, Val Accuracy: 0.9483 Epoch 787/1200, Train Loss: 0.0837, Train Accuracy: 0.9739, Val Loss: 0.1275, Val Accuracy: 0.9489 Epoch 788/1200, Train Loss: 0.0829, Train Accuracy: 0.9739, Val Loss: 0.1274, Val Accuracy: 0.9485 Epoch 789/1200, Train Loss: 0.0811, Train Accuracy: 0.9742, Val Loss: 0.1274, Val Accuracy: 0.9487 Epoch 790/1200, Train Loss: 0.0838, Train Accuracy: 0.9726, Val Loss: 0.1274, Val Accuracy: 0.9485 Epoch 791/1200, Train Loss: 0.0831, Train Accuracy: 0.9739, Val Loss: 0.1273, Val Accuracy: 0.9487 Epoch 792/1200, Train Loss: 0.0831, Train Accuracy: 0.9731, Val Loss: 0.1273, Val Accuracy: 0.9485 Epoch 793/1200, Train Loss: 0.0825, Train Accuracy: 0.9730, Val Loss: 0.1273, Val Accuracy: 0.9487 Epoch 794/1200, Train Loss: 0.0821, Train Accuracy: 0.9736, Val Loss: 0.1272, Val Accuracy: 0.9487 Epoch 795/1200, Train Loss: 0.0813, Train Accuracy: 0.9736, Val Loss: 0.1272, Val Accuracy: 0.9487 Epoch 796/1200, Train Loss: 0.0804, Train Accuracy: 0.9741, Val Loss: 0.1272, Val Accuracy: 0.9487 Epoch 797/1200, Train Loss: 0.0830, Train Accuracy: 0.9729, Val Loss: 0.1271, Val Accuracy: 0.9487 Epoch 798/1200, Train Loss: 0.0806, Train Accuracy: 0.9728, Val Loss: 0.1272, Val Accuracy: 0.9489 Epoch 799/1200, Train Loss: 0.0801, Train Accuracy: 0.9740, Val Loss: 0.1272, Val Accuracy: 0.9487 Epoch 800/1200, Train Loss: 0.0811, Train Accuracy: 0.9742, Val Loss: 0.1272, Val Accuracy: 0.9487 Epoch 801/1200, Train Loss: 0.0819, Train Accuracy: 0.9741, Val Loss: 0.1272, Val Accuracy: 0.9485 Epoch 802/1200, Train Loss: 0.0812, Train Accuracy: 0.9736, Val Loss: 0.1271, Val Accuracy: 0.9487 Epoch 803/1200, Train Loss: 0.0808, Train Accuracy: 0.9741, Val Loss: 0.1271, Val Accuracy: 0.9487 Epoch 804/1200, Train Loss: 0.0800, Train Accuracy: 0.9743, Val Loss: 0.1270, Val Accuracy: 0.9487 Epoch 805/1200, Train Loss: 0.0785, Train Accuracy: 0.9755, Val Loss: 0.1271, Val Accuracy: 0.9487 Epoch 806/1200, Train Loss: 0.0779, Train Accuracy: 0.9749, Val Loss: 0.1272, Val Accuracy: 0.9487 Epoch 807/1200, Train Loss: 0.0802, Train Accuracy: 0.9743, Val Loss: 0.1271, Val Accuracy: 0.9485 Epoch 808/1200, Train Loss: 0.0812, Train Accuracy: 0.9725, Val Loss: 0.1270, Val Accuracy: 0.9485 Epoch 809/1200, Train Loss: 0.0777, Train Accuracy: 0.9756, Val Loss: 0.1271, Val Accuracy: 0.9487 Epoch 810/1200, Train Loss: 0.0791, Train Accuracy: 0.9752, Val Loss: 0.1270, Val Accuracy: 0.9485 Epoch 811/1200, Train Loss: 0.0800, Train Accuracy: 0.9741, Val Loss: 0.1268, Val Accuracy: 0.9491 Epoch 812/1200, Train Loss: 0.0792, Train Accuracy: 0.9743, Val Loss: 0.1268, Val Accuracy: 0.9493 Epoch 813/1200, Train Loss: 0.0791, Train Accuracy: 0.9737, Val Loss: 0.1267, Val Accuracy: 0.9493 Epoch 814/1200, Train Loss: 0.0786, Train Accuracy: 0.9743, Val Loss: 0.1267, Val Accuracy: 0.9493 Epoch 815/1200, Train Loss: 0.0779, Train Accuracy: 0.9747, Val Loss: 0.1267, Val Accuracy: 0.9491 Epoch 816/1200, Train Loss: 0.0777, Train Accuracy: 0.9749, Val Loss: 0.1266, Val Accuracy: 0.9493 Epoch 817/1200, Train Loss: 0.0789, Train Accuracy: 0.9745, Val Loss: 0.1266, Val Accuracy: 0.9493 Epoch 818/1200, Train Loss: 0.0780, Train Accuracy: 0.9755, Val Loss: 0.1266, Val Accuracy: 0.9493 Epoch 819/1200, Train Loss: 0.0790, Train Accuracy: 0.9738, Val Loss: 0.1267, Val Accuracy: 0.9491 Epoch 820/1200, Train Loss: 0.0787, Train Accuracy: 0.9748, Val Loss: 0.1266, Val Accuracy: 0.9491 Epoch 821/1200, Train Loss: 0.0761, Train Accuracy: 0.9765, Val Loss: 0.1266, Val Accuracy: 0.9493 Epoch 822/1200, Train Loss: 0.0778, Train Accuracy: 0.9752, Val Loss: 0.1265, Val Accuracy: 0.9493 Epoch 823/1200, Train Loss: 0.0772, Train Accuracy: 0.9753, Val Loss: 0.1265, Val Accuracy: 0.9493 Epoch 824/1200, Train Loss: 0.0769, Train Accuracy: 0.9760, Val Loss: 0.1264, Val Accuracy: 0.9491 Epoch 825/1200, Train Loss: 0.0761, Train Accuracy: 0.9762, Val Loss: 0.1264, Val Accuracy: 0.9493 Epoch 826/1200, Train Loss: 0.0755, Train Accuracy: 0.9759, Val Loss: 0.1263, Val Accuracy: 0.9493 Epoch 827/1200, Train Loss: 0.0758, Train Accuracy: 0.9758, Val Loss: 0.1263, Val Accuracy: 0.9495 Epoch 828/1200, Train Loss: 0.0773, Train Accuracy: 0.9753, Val Loss: 0.1263, Val Accuracy: 0.9493 Epoch 829/1200, Train Loss: 0.0758, Train Accuracy: 0.9761, Val Loss: 0.1262, Val Accuracy: 0.9497 Epoch 830/1200, Train Loss: 0.0751, Train Accuracy: 0.9756, Val Loss: 0.1261, Val Accuracy: 0.9498 Epoch 831/1200, Train Loss: 0.0759, Train Accuracy: 0.9753, Val Loss: 0.1261, Val Accuracy: 0.9498 Epoch 832/1200, Train Loss: 0.0750, Train Accuracy: 0.9760, Val Loss: 0.1261, Val Accuracy: 0.9498 Epoch 833/1200, Train Loss: 0.0752, Train Accuracy: 0.9759, Val Loss: 0.1260, Val Accuracy: 0.9500 Epoch 834/1200, Train Loss: 0.0754, Train Accuracy: 0.9765, Val Loss: 0.1259, Val Accuracy: 0.9502 Epoch 835/1200, Train Loss: 0.0752, Train Accuracy: 0.9762, Val Loss: 0.1260, Val Accuracy: 0.9498 Epoch 836/1200, Train Loss: 0.0746, Train Accuracy: 0.9759, Val Loss: 0.1261, Val Accuracy: 0.9500 Epoch 837/1200, Train Loss: 0.0744, Train Accuracy: 0.9763, Val Loss: 0.1260, Val Accuracy: 0.9500 Epoch 838/1200, Train Loss: 0.0732, Train Accuracy: 0.9762, Val Loss: 0.1260, Val Accuracy: 0.9502 Epoch 839/1200, Train Loss: 0.0746, Train Accuracy: 0.9761, Val Loss: 0.1261, Val Accuracy: 0.9500 Epoch 840/1200, Train Loss: 0.0725, Train Accuracy: 0.9772, Val Loss: 0.1260, Val Accuracy: 0.9500 Epoch 841/1200, Train Loss: 0.0733, Train Accuracy: 0.9759, Val Loss: 0.1261, Val Accuracy: 0.9498 Epoch 842/1200, Train Loss: 0.0728, Train Accuracy: 0.9773, Val Loss: 0.1261, Val Accuracy: 0.9498 Epoch 843/1200, Train Loss: 0.0734, Train Accuracy: 0.9772, Val Loss: 0.1260, Val Accuracy: 0.9500 Epoch 844/1200, Train Loss: 0.0734, Train Accuracy: 0.9771, Val Loss: 0.1259, Val Accuracy: 0.9500 Epoch 845/1200, Train Loss: 0.0712, Train Accuracy: 0.9785, Val Loss: 0.1258, Val Accuracy: 0.9498 Epoch 846/1200, Train Loss: 0.0736, Train Accuracy: 0.9772, Val Loss: 0.1259, Val Accuracy: 0.9500 Epoch 847/1200, Train Loss: 0.0724, Train Accuracy: 0.9766, Val Loss: 0.1258, Val Accuracy: 0.9497 Epoch 848/1200, Train Loss: 0.0718, Train Accuracy: 0.9773, Val Loss: 0.1259, Val Accuracy: 0.9500 Epoch 849/1200, Train Loss: 0.0714, Train Accuracy: 0.9777, Val Loss: 0.1260, Val Accuracy: 0.9498 Epoch 850/1200, Train Loss: 0.0712, Train Accuracy: 0.9781, Val Loss: 0.1260, Val Accuracy: 0.9498 Epoch 851/1200, Train Loss: 0.0722, Train Accuracy: 0.9772, Val Loss: 0.1260, Val Accuracy: 0.9498 Epoch 852/1200, Train Loss: 0.0706, Train Accuracy: 0.9782, Val Loss: 0.1261, Val Accuracy: 0.9500 Epoch 853/1200, Train Loss: 0.0714, Train Accuracy: 0.9788, Val Loss: 0.1261, Val Accuracy: 0.9502 Epoch 854/1200, Train Loss: 0.0723, Train Accuracy: 0.9770, Val Loss: 0.1258, Val Accuracy: 0.9502 Epoch 855/1200, Train Loss: 0.0715, Train Accuracy: 0.9779, Val Loss: 0.1258, Val Accuracy: 0.9498 Epoch 856/1200, Train Loss: 0.0719, Train Accuracy: 0.9778, Val Loss: 0.1259, Val Accuracy: 0.9500 Epoch 857/1200, Train Loss: 0.0708, Train Accuracy: 0.9776, Val Loss: 0.1258, Val Accuracy: 0.9500 Epoch 858/1200, Train Loss: 0.0721, Train Accuracy: 0.9762, Val Loss: 0.1256, Val Accuracy: 0.9502 Epoch 859/1200, Train Loss: 0.0709, Train Accuracy: 0.9780, Val Loss: 0.1256, Val Accuracy: 0.9502 Epoch 860/1200, Train Loss: 0.0691, Train Accuracy: 0.9786, Val Loss: 0.1256, Val Accuracy: 0.9502 Epoch 861/1200, Train Loss: 0.0698, Train Accuracy: 0.9776, Val Loss: 0.1256, Val Accuracy: 0.9502 Epoch 862/1200, Train Loss: 0.0695, Train Accuracy: 0.9781, Val Loss: 0.1257, Val Accuracy: 0.9502 Epoch 863/1200, Train Loss: 0.0707, Train Accuracy: 0.9781, Val Loss: 0.1257, Val Accuracy: 0.9504 Epoch 864/1200, Train Loss: 0.0699, Train Accuracy: 0.9783, Val Loss: 0.1256, Val Accuracy: 0.9502 Epoch 865/1200, Train Loss: 0.0696, Train Accuracy: 0.9781, Val Loss: 0.1256, Val Accuracy: 0.9502 Epoch 866/1200, Train Loss: 0.0710, Train Accuracy: 0.9780, Val Loss: 0.1256, Val Accuracy: 0.9504 Epoch 867/1200, Train Loss: 0.0713, Train Accuracy: 0.9764, Val Loss: 0.1256, Val Accuracy: 0.9508 Epoch 868/1200, Train Loss: 0.0688, Train Accuracy: 0.9790, Val Loss: 0.1257, Val Accuracy: 0.9502 Epoch 869/1200, Train Loss: 0.0685, Train Accuracy: 0.9792, Val Loss: 0.1256, Val Accuracy: 0.9502 Epoch 870/1200, Train Loss: 0.0694, Train Accuracy: 0.9780, Val Loss: 0.1255, Val Accuracy: 0.9504 Epoch 871/1200, Train Loss: 0.0701, Train Accuracy: 0.9784, Val Loss: 0.1256, Val Accuracy: 0.9504 Epoch 872/1200, Train Loss: 0.0670, Train Accuracy: 0.9807, Val Loss: 0.1256, Val Accuracy: 0.9502 Epoch 873/1200, Train Loss: 0.0681, Train Accuracy: 0.9789, Val Loss: 0.1258, Val Accuracy: 0.9508 Epoch 874/1200, Train Loss: 0.0679, Train Accuracy: 0.9784, Val Loss: 0.1256, Val Accuracy: 0.9502 Epoch 875/1200, Train Loss: 0.0690, Train Accuracy: 0.9782, Val Loss: 0.1257, Val Accuracy: 0.9506 Epoch 876/1200, Train Loss: 0.0675, Train Accuracy: 0.9790, Val Loss: 0.1255, Val Accuracy: 0.9504 Epoch 877/1200, Train Loss: 0.0681, Train Accuracy: 0.9783, Val Loss: 0.1257, Val Accuracy: 0.9506 Epoch 878/1200, Train Loss: 0.0686, Train Accuracy: 0.9792, Val Loss: 0.1256, Val Accuracy: 0.9508 Epoch 879/1200, Train Loss: 0.0683, Train Accuracy: 0.9786, Val Loss: 0.1257, Val Accuracy: 0.9506 Epoch 880/1200, Train Loss: 0.0666, Train Accuracy: 0.9806, Val Loss: 0.1257, Val Accuracy: 0.9508 Epoch 881/1200, Train Loss: 0.0681, Train Accuracy: 0.9787, Val Loss: 0.1255, Val Accuracy: 0.9510 Epoch 882/1200, Train Loss: 0.0667, Train Accuracy: 0.9799, Val Loss: 0.1255, Val Accuracy: 0.9508 Epoch 883/1200, Train Loss: 0.0661, Train Accuracy: 0.9792, Val Loss: 0.1255, Val Accuracy: 0.9508 Epoch 884/1200, Train Loss: 0.0680, Train Accuracy: 0.9793, Val Loss: 0.1254, Val Accuracy: 0.9510 Epoch 885/1200, Train Loss: 0.0658, Train Accuracy: 0.9793, Val Loss: 0.1255, Val Accuracy: 0.9506 Epoch 886/1200, Train Loss: 0.0681, Train Accuracy: 0.9793, Val Loss: 0.1254, Val Accuracy: 0.9510 Epoch 887/1200, Train Loss: 0.0673, Train Accuracy: 0.9794, Val Loss: 0.1253, Val Accuracy: 0.9510 Epoch 888/1200, Train Loss: 0.0641, Train Accuracy: 0.9805, Val Loss: 0.1254, Val Accuracy: 0.9508 Epoch 889/1200, Train Loss: 0.0681, Train Accuracy: 0.9788, Val Loss: 0.1253, Val Accuracy: 0.9510 Epoch 890/1200, Train Loss: 0.0660, Train Accuracy: 0.9797, Val Loss: 0.1254, Val Accuracy: 0.9514 Epoch 891/1200, Train Loss: 0.0656, Train Accuracy: 0.9802, Val Loss: 0.1254, Val Accuracy: 0.9512 Epoch 892/1200, Train Loss: 0.0654, Train Accuracy: 0.9789, Val Loss: 0.1252, Val Accuracy: 0.9508 Epoch 893/1200, Train Loss: 0.0661, Train Accuracy: 0.9796, Val Loss: 0.1252, Val Accuracy: 0.9508 Epoch 894/1200, Train Loss: 0.0650, Train Accuracy: 0.9800, Val Loss: 0.1253, Val Accuracy: 0.9508 Epoch 895/1200, Train Loss: 0.0649, Train Accuracy: 0.9803, Val Loss: 0.1253, Val Accuracy: 0.9510 Epoch 896/1200, Train Loss: 0.0642, Train Accuracy: 0.9811, Val Loss: 0.1253, Val Accuracy: 0.9510 Epoch 897/1200, Train Loss: 0.0649, Train Accuracy: 0.9795, Val Loss: 0.1253, Val Accuracy: 0.9510 Epoch 898/1200, Train Loss: 0.0653, Train Accuracy: 0.9797, Val Loss: 0.1253, Val Accuracy: 0.9510 Epoch 899/1200, Train Loss: 0.0652, Train Accuracy: 0.9791, Val Loss: 0.1252, Val Accuracy: 0.9510 Epoch 900/1200, Train Loss: 0.0646, Train Accuracy: 0.9799, Val Loss: 0.1252, Val Accuracy: 0.9510 Epoch 901/1200, Train Loss: 0.0643, Train Accuracy: 0.9802, Val Loss: 0.1252, Val Accuracy: 0.9510 Epoch 902/1200, Train Loss: 0.0638, Train Accuracy: 0.9806, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 903/1200, Train Loss: 0.0632, Train Accuracy: 0.9804, Val Loss: 0.1251, Val Accuracy: 0.9510 Epoch 904/1200, Train Loss: 0.0633, Train Accuracy: 0.9815, Val Loss: 0.1251, Val Accuracy: 0.9510 Epoch 905/1200, Train Loss: 0.0631, Train Accuracy: 0.9801, Val Loss: 0.1253, Val Accuracy: 0.9512 Epoch 906/1200, Train Loss: 0.0645, Train Accuracy: 0.9793, Val Loss: 0.1254, Val Accuracy: 0.9514 Epoch 907/1200, Train Loss: 0.0620, Train Accuracy: 0.9816, Val Loss: 0.1252, Val Accuracy: 0.9516 Epoch 908/1200, Train Loss: 0.0645, Train Accuracy: 0.9813, Val Loss: 0.1252, Val Accuracy: 0.9516 Epoch 909/1200, Train Loss: 0.0631, Train Accuracy: 0.9807, Val Loss: 0.1252, Val Accuracy: 0.9516 Epoch 910/1200, Train Loss: 0.0631, Train Accuracy: 0.9817, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 911/1200, Train Loss: 0.0633, Train Accuracy: 0.9809, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 912/1200, Train Loss: 0.0633, Train Accuracy: 0.9808, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 913/1200, Train Loss: 0.0614, Train Accuracy: 0.9812, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 914/1200, Train Loss: 0.0627, Train Accuracy: 0.9806, Val Loss: 0.1254, Val Accuracy: 0.9514 Epoch 915/1200, Train Loss: 0.0620, Train Accuracy: 0.9808, Val Loss: 0.1252, Val Accuracy: 0.9512 Epoch 916/1200, Train Loss: 0.0621, Train Accuracy: 0.9808, Val Loss: 0.1253, Val Accuracy: 0.9512 Epoch 917/1200, Train Loss: 0.0609, Train Accuracy: 0.9818, Val Loss: 0.1253, Val Accuracy: 0.9516 Epoch 918/1200, Train Loss: 0.0635, Train Accuracy: 0.9808, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 919/1200, Train Loss: 0.0628, Train Accuracy: 0.9811, Val Loss: 0.1253, Val Accuracy: 0.9514 Epoch 920/1200, Train Loss: 0.0608, Train Accuracy: 0.9819, Val Loss: 0.1253, Val Accuracy: 0.9514 Epoch 921/1200, Train Loss: 0.0614, Train Accuracy: 0.9811, Val Loss: 0.1252, Val Accuracy: 0.9516 Epoch 922/1200, Train Loss: 0.0608, Train Accuracy: 0.9813, Val Loss: 0.1251, Val Accuracy: 0.9514 Epoch 923/1200, Train Loss: 0.0630, Train Accuracy: 0.9813, Val Loss: 0.1252, Val Accuracy: 0.9516 Epoch 924/1200, Train Loss: 0.0612, Train Accuracy: 0.9816, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 925/1200, Train Loss: 0.0622, Train Accuracy: 0.9810, Val Loss: 0.1251, Val Accuracy: 0.9514 Epoch 926/1200, Train Loss: 0.0606, Train Accuracy: 0.9816, Val Loss: 0.1251, Val Accuracy: 0.9514 Epoch 927/1200, Train Loss: 0.0595, Train Accuracy: 0.9820, Val Loss: 0.1250, Val Accuracy: 0.9514 Epoch 928/1200, Train Loss: 0.0592, Train Accuracy: 0.9827, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 929/1200, Train Loss: 0.0599, Train Accuracy: 0.9826, Val Loss: 0.1251, Val Accuracy: 0.9516 Epoch 930/1200, Train Loss: 0.0596, Train Accuracy: 0.9811, Val Loss: 0.1251, Val Accuracy: 0.9514 Epoch 931/1200, Train Loss: 0.0605, Train Accuracy: 0.9808, Val Loss: 0.1251, Val Accuracy: 0.9514 Epoch 932/1200, Train Loss: 0.0603, Train Accuracy: 0.9816, Val Loss: 0.1250, Val Accuracy: 0.9512 Epoch 933/1200, Train Loss: 0.0603, Train Accuracy: 0.9810, Val Loss: 0.1249, Val Accuracy: 0.9514 Epoch 934/1200, Train Loss: 0.0588, Train Accuracy: 0.9828, Val Loss: 0.1250, Val Accuracy: 0.9514 Epoch 935/1200, Train Loss: 0.0590, Train Accuracy: 0.9820, Val Loss: 0.1249, Val Accuracy: 0.9512 Epoch 936/1200, Train Loss: 0.0596, Train Accuracy: 0.9821, Val Loss: 0.1249, Val Accuracy: 0.9514 Epoch 937/1200, Train Loss: 0.0575, Train Accuracy: 0.9830, Val Loss: 0.1250, Val Accuracy: 0.9514 Epoch 938/1200, Train Loss: 0.0586, Train Accuracy: 0.9825, Val Loss: 0.1249, Val Accuracy: 0.9514 Epoch 939/1200, Train Loss: 0.0590, Train Accuracy: 0.9828, Val Loss: 0.1249, Val Accuracy: 0.9516 Epoch 940/1200, Train Loss: 0.0582, Train Accuracy: 0.9829, Val Loss: 0.1250, Val Accuracy: 0.9516 Epoch 941/1200, Train Loss: 0.0586, Train Accuracy: 0.9827, Val Loss: 0.1250, Val Accuracy: 0.9514 Epoch 942/1200, Train Loss: 0.0577, Train Accuracy: 0.9830, Val Loss: 0.1250, Val Accuracy: 0.9514 Epoch 943/1200, Train Loss: 0.0580, Train Accuracy: 0.9816, Val Loss: 0.1251, Val Accuracy: 0.9514 Epoch 944/1200, Train Loss: 0.0566, Train Accuracy: 0.9829, Val Loss: 0.1249, Val Accuracy: 0.9518 Epoch 945/1200, Train Loss: 0.0586, Train Accuracy: 0.9822, Val Loss: 0.1253, Val Accuracy: 0.9518 Epoch 946/1200, Train Loss: 0.0587, Train Accuracy: 0.9827, Val Loss: 0.1252, Val Accuracy: 0.9514 Epoch 947/1200, Train Loss: 0.0570, Train Accuracy: 0.9829, Val Loss: 0.1252, Val Accuracy: 0.9516 Epoch 948/1200, Train Loss: 0.0559, Train Accuracy: 0.9841, Val Loss: 0.1252, Val Accuracy: 0.9516 Epoch 949/1200, Train Loss: 0.0576, Train Accuracy: 0.9818, Val Loss: 0.1252, Val Accuracy: 0.9516 Epoch 950/1200, Train Loss: 0.0570, Train Accuracy: 0.9830, Val Loss: 0.1254, Val Accuracy: 0.9520 Epoch 951/1200, Train Loss: 0.0552, Train Accuracy: 0.9833, Val Loss: 0.1253, Val Accuracy: 0.9518 Epoch 952/1200, Train Loss: 0.0559, Train Accuracy: 0.9833, Val Loss: 0.1253, Val Accuracy: 0.9516 Epoch 953/1200, Train Loss: 0.0566, Train Accuracy: 0.9834, Val Loss: 0.1254, Val Accuracy: 0.9516 Epoch 954/1200, Train Loss: 0.0559, Train Accuracy: 0.9835, Val Loss: 0.1254, Val Accuracy: 0.9520 Epoch 955/1200, Train Loss: 0.0564, Train Accuracy: 0.9826, Val Loss: 0.1253, Val Accuracy: 0.9518 Epoch 956/1200, Train Loss: 0.0554, Train Accuracy: 0.9840, Val Loss: 0.1253, Val Accuracy: 0.9522 Epoch 957/1200, Train Loss: 0.0572, Train Accuracy: 0.9828, Val Loss: 0.1253, Val Accuracy: 0.9520 Epoch 958/1200, Train Loss: 0.0571, Train Accuracy: 0.9831, Val Loss: 0.1252, Val Accuracy: 0.9518 Epoch 959/1200, Train Loss: 0.0567, Train Accuracy: 0.9837, Val Loss: 0.1252, Val Accuracy: 0.9516 Epoch 960/1200, Train Loss: 0.0566, Train Accuracy: 0.9831, Val Loss: 0.1251, Val Accuracy: 0.9516 Epoch 961/1200, Train Loss: 0.0545, Train Accuracy: 0.9840, Val Loss: 0.1251, Val Accuracy: 0.9516 Epoch 962/1200, Train Loss: 0.0565, Train Accuracy: 0.9832, Val Loss: 0.1252, Val Accuracy: 0.9520 Epoch 963/1200, Train Loss: 0.0563, Train Accuracy: 0.9831, Val Loss: 0.1251, Val Accuracy: 0.9516 Epoch 964/1200, Train Loss: 0.0569, Train Accuracy: 0.9827, Val Loss: 0.1251, Val Accuracy: 0.9516 Epoch 965/1200, Train Loss: 0.0553, Train Accuracy: 0.9836, Val Loss: 0.1251, Val Accuracy: 0.9516 Epoch 966/1200, Train Loss: 0.0551, Train Accuracy: 0.9840, Val Loss: 0.1252, Val Accuracy: 0.9520 Epoch 967/1200, Train Loss: 0.0532, Train Accuracy: 0.9852, Val Loss: 0.1252, Val Accuracy: 0.9518 Epoch 968/1200, Train Loss: 0.0546, Train Accuracy: 0.9842, Val Loss: 0.1251, Val Accuracy: 0.9518 Epoch 969/1200, Train Loss: 0.0537, Train Accuracy: 0.9840, Val Loss: 0.1251, Val Accuracy: 0.9518 Epoch 970/1200, Train Loss: 0.0543, Train Accuracy: 0.9839, Val Loss: 0.1253, Val Accuracy: 0.9518 Epoch 971/1200, Train Loss: 0.0549, Train Accuracy: 0.9839, Val Loss: 0.1253, Val Accuracy: 0.9520 Epoch 972/1200, Train Loss: 0.0527, Train Accuracy: 0.9852, Val Loss: 0.1253, Val Accuracy: 0.9520 Epoch 973/1200, Train Loss: 0.0539, Train Accuracy: 0.9852, Val Loss: 0.1252, Val Accuracy: 0.9518 Epoch 974/1200, Train Loss: 0.0531, Train Accuracy: 0.9849, Val Loss: 0.1252, Val Accuracy: 0.9518 Epoch 975/1200, Train Loss: 0.0536, Train Accuracy: 0.9840, Val Loss: 0.1253, Val Accuracy: 0.9518 Epoch 976/1200, Train Loss: 0.0527, Train Accuracy: 0.9848, Val Loss: 0.1253, Val Accuracy: 0.9518 Epoch 977/1200, Train Loss: 0.0531, Train Accuracy: 0.9843, Val Loss: 0.1254, Val Accuracy: 0.9522 Epoch 978/1200, Train Loss: 0.0534, Train Accuracy: 0.9840, Val Loss: 0.1252, Val Accuracy: 0.9520 Epoch 979/1200, Train Loss: 0.0534, Train Accuracy: 0.9843, Val Loss: 0.1253, Val Accuracy: 0.9520 Epoch 980/1200, Train Loss: 0.0531, Train Accuracy: 0.9845, Val Loss: 0.1253, Val Accuracy: 0.9522 Epoch 981/1200, Train Loss: 0.0530, Train Accuracy: 0.9838, Val Loss: 0.1252, Val Accuracy: 0.9520 Epoch 982/1200, Train Loss: 0.0534, Train Accuracy: 0.9847, Val Loss: 0.1252, Val Accuracy: 0.9524 Epoch 983/1200, Train Loss: 0.0551, Train Accuracy: 0.9838, Val Loss: 0.1253, Val Accuracy: 0.9524 Epoch 984/1200, Train Loss: 0.0527, Train Accuracy: 0.9845, Val Loss: 0.1254, Val Accuracy: 0.9522 Epoch 985/1200, Train Loss: 0.0518, Train Accuracy: 0.9848, Val Loss: 0.1255, Val Accuracy: 0.9524 Epoch 986/1200, Train Loss: 0.0526, Train Accuracy: 0.9847, Val Loss: 0.1253, Val Accuracy: 0.9520 Epoch 987/1200, Train Loss: 0.0524, Train Accuracy: 0.9847, Val Loss: 0.1253, Val Accuracy: 0.9520 Epoch 988/1200, Train Loss: 0.0528, Train Accuracy: 0.9848, Val Loss: 0.1254, Val Accuracy: 0.9520 Epoch 989/1200, Train Loss: 0.0531, Train Accuracy: 0.9840, Val Loss: 0.1254, Val Accuracy: 0.9524 Epoch 990/1200, Train Loss: 0.0510, Train Accuracy: 0.9855, Val Loss: 0.1254, Val Accuracy: 0.9524 Epoch 991/1200, Train Loss: 0.0521, Train Accuracy: 0.9849, Val Loss: 0.1256, Val Accuracy: 0.9526 Epoch 992/1200, Train Loss: 0.0507, Train Accuracy: 0.9856, Val Loss: 0.1253, Val Accuracy: 0.9520 Epoch 993/1200, Train Loss: 0.0517, Train Accuracy: 0.9853, Val Loss: 0.1253, Val Accuracy: 0.9520 Epoch 994/1200, Train Loss: 0.0514, Train Accuracy: 0.9847, Val Loss: 0.1253, Val Accuracy: 0.9520 Epoch 995/1200, Train Loss: 0.0526, Train Accuracy: 0.9845, Val Loss: 0.1255, Val Accuracy: 0.9524 Epoch 996/1200, Train Loss: 0.0512, Train Accuracy: 0.9854, Val Loss: 0.1254, Val Accuracy: 0.9526 Epoch 997/1200, Train Loss: 0.0524, Train Accuracy: 0.9849, Val Loss: 0.1254, Val Accuracy: 0.9528 Epoch 998/1200, Train Loss: 0.0529, Train Accuracy: 0.9851, Val Loss: 0.1252, Val Accuracy: 0.9520 Epoch 999/1200, Train Loss: 0.0502, Train Accuracy: 0.9865, Val Loss: 0.1255, Val Accuracy: 0.9528 Epoch 1000/1200, Train Loss: 0.0523, Train Accuracy: 0.9848, Val Loss: 0.1254, Val Accuracy: 0.9528 Epoch 1001/1200, Train Loss: 0.0506, Train Accuracy: 0.9848, Val Loss: 0.1255, Val Accuracy: 0.9526 Epoch 1002/1200, Train Loss: 0.0504, Train Accuracy: 0.9861, Val Loss: 0.1254, Val Accuracy: 0.9526 Epoch 1003/1200, Train Loss: 0.0497, Train Accuracy: 0.9861, Val Loss: 0.1254, Val Accuracy: 0.9524 Epoch 1004/1200, Train Loss: 0.0501, Train Accuracy: 0.9848, Val Loss: 0.1255, Val Accuracy: 0.9526 Epoch 1005/1200, Train Loss: 0.0505, Train Accuracy: 0.9859, Val Loss: 0.1254, Val Accuracy: 0.9526 Epoch 1006/1200, Train Loss: 0.0495, Train Accuracy: 0.9862, Val Loss: 0.1254, Val Accuracy: 0.9526 Epoch 1007/1200, Train Loss: 0.0502, Train Accuracy: 0.9851, Val Loss: 0.1256, Val Accuracy: 0.9526 Epoch 1008/1200, Train Loss: 0.0498, Train Accuracy: 0.9860, Val Loss: 0.1256, Val Accuracy: 0.9528 Epoch 1009/1200, Train Loss: 0.0489, Train Accuracy: 0.9861, Val Loss: 0.1258, Val Accuracy: 0.9522 Epoch 1010/1200, Train Loss: 0.0484, Train Accuracy: 0.9864, Val Loss: 0.1256, Val Accuracy: 0.9528 Epoch 1011/1200, Train Loss: 0.0501, Train Accuracy: 0.9855, Val Loss: 0.1257, Val Accuracy: 0.9524 Epoch 1012/1200, Train Loss: 0.0489, Train Accuracy: 0.9860, Val Loss: 0.1257, Val Accuracy: 0.9524 Epoch 1013/1200, Train Loss: 0.0483, Train Accuracy: 0.9866, Val Loss: 0.1256, Val Accuracy: 0.9526 Epoch 1014/1200, Train Loss: 0.0496, Train Accuracy: 0.9854, Val Loss: 0.1255, Val Accuracy: 0.9526 Epoch 1015/1200, Train Loss: 0.0486, Train Accuracy: 0.9867, Val Loss: 0.1257, Val Accuracy: 0.9526 Epoch 1016/1200, Train Loss: 0.0488, Train Accuracy: 0.9862, Val Loss: 0.1256, Val Accuracy: 0.9526 Epoch 1017/1200, Train Loss: 0.0487, Train Accuracy: 0.9866, Val Loss: 0.1255, Val Accuracy: 0.9530 Epoch 1018/1200, Train Loss: 0.0481, Train Accuracy: 0.9859, Val Loss: 0.1257, Val Accuracy: 0.9526 Epoch 1019/1200, Train Loss: 0.0499, Train Accuracy: 0.9862, Val Loss: 0.1256, Val Accuracy: 0.9526 Epoch 1020/1200, Train Loss: 0.0497, Train Accuracy: 0.9862, Val Loss: 0.1257, Val Accuracy: 0.9526 Epoch 1021/1200, Train Loss: 0.0478, Train Accuracy: 0.9865, Val Loss: 0.1258, Val Accuracy: 0.9530 Epoch 1022/1200, Train Loss: 0.0480, Train Accuracy: 0.9864, Val Loss: 0.1258, Val Accuracy: 0.9528 Epoch 1023/1200, Train Loss: 0.0489, Train Accuracy: 0.9867, Val Loss: 0.1258, Val Accuracy: 0.9530 Epoch 1024/1200, Train Loss: 0.0478, Train Accuracy: 0.9866, Val Loss: 0.1259, Val Accuracy: 0.9528 Epoch 1025/1200, Train Loss: 0.0487, Train Accuracy: 0.9860, Val Loss: 0.1259, Val Accuracy: 0.9528 Epoch 1026/1200, Train Loss: 0.0473, Train Accuracy: 0.9866, Val Loss: 0.1262, Val Accuracy: 0.9526 Epoch 1027/1200, Train Loss: 0.0470, Train Accuracy: 0.9869, Val Loss: 0.1260, Val Accuracy: 0.9528 Epoch 1028/1200, Train Loss: 0.0473, Train Accuracy: 0.9868, Val Loss: 0.1261, Val Accuracy: 0.9528 Epoch 1029/1200, Train Loss: 0.0480, Train Accuracy: 0.9854, Val Loss: 0.1261, Val Accuracy: 0.9524 Epoch 1030/1200, Train Loss: 0.0483, Train Accuracy: 0.9860, Val Loss: 0.1260, Val Accuracy: 0.9528 Epoch 1031/1200, Train Loss: 0.0480, Train Accuracy: 0.9868, Val Loss: 0.1259, Val Accuracy: 0.9530 Epoch 1032/1200, Train Loss: 0.0463, Train Accuracy: 0.9874, Val Loss: 0.1260, Val Accuracy: 0.9530 Epoch 1033/1200, Train Loss: 0.0468, Train Accuracy: 0.9869, Val Loss: 0.1262, Val Accuracy: 0.9526 Epoch 1034/1200, Train Loss: 0.0476, Train Accuracy: 0.9861, Val Loss: 0.1263, Val Accuracy: 0.9526 Epoch 1035/1200, Train Loss: 0.0471, Train Accuracy: 0.9870, Val Loss: 0.1260, Val Accuracy: 0.9530 Epoch 1036/1200, Train Loss: 0.0471, Train Accuracy: 0.9869, Val Loss: 0.1258, Val Accuracy: 0.9531 Epoch 1037/1200, Train Loss: 0.0452, Train Accuracy: 0.9870, Val Loss: 0.1259, Val Accuracy: 0.9530 Epoch 1038/1200, Train Loss: 0.0458, Train Accuracy: 0.9875, Val Loss: 0.1260, Val Accuracy: 0.9530 Epoch 1039/1200, Train Loss: 0.0476, Train Accuracy: 0.9864, Val Loss: 0.1260, Val Accuracy: 0.9530 Epoch 1040/1200, Train Loss: 0.0467, Train Accuracy: 0.9869, Val Loss: 0.1261, Val Accuracy: 0.9530 Epoch 1041/1200, Train Loss: 0.0464, Train Accuracy: 0.9880, Val Loss: 0.1260, Val Accuracy: 0.9530 Epoch 1042/1200, Train Loss: 0.0473, Train Accuracy: 0.9860, Val Loss: 0.1261, Val Accuracy: 0.9530 Epoch 1043/1200, Train Loss: 0.0458, Train Accuracy: 0.9876, Val Loss: 0.1263, Val Accuracy: 0.9528 Epoch 1044/1200, Train Loss: 0.0466, Train Accuracy: 0.9869, Val Loss: 0.1262, Val Accuracy: 0.9530 Epoch 1045/1200, Train Loss: 0.0459, Train Accuracy: 0.9875, Val Loss: 0.1263, Val Accuracy: 0.9530 Epoch 1046/1200, Train Loss: 0.0456, Train Accuracy: 0.9874, Val Loss: 0.1265, Val Accuracy: 0.9530 Epoch 1047/1200, Train Loss: 0.0451, Train Accuracy: 0.9884, Val Loss: 0.1264, Val Accuracy: 0.9531 Epoch 1048/1200, Train Loss: 0.0448, Train Accuracy: 0.9872, Val Loss: 0.1261, Val Accuracy: 0.9530 Epoch 1049/1200, Train Loss: 0.0450, Train Accuracy: 0.9874, Val Loss: 0.1263, Val Accuracy: 0.9530 Epoch 1050/1200, Train Loss: 0.0458, Train Accuracy: 0.9878, Val Loss: 0.1261, Val Accuracy: 0.9530 Epoch 1051/1200, Train Loss: 0.0453, Train Accuracy: 0.9876, Val Loss: 0.1260, Val Accuracy: 0.9530 Epoch 1052/1200, Train Loss: 0.0446, Train Accuracy: 0.9878, Val Loss: 0.1262, Val Accuracy: 0.9530 Epoch 1053/1200, Train Loss: 0.0448, Train Accuracy: 0.9871, Val Loss: 0.1263, Val Accuracy: 0.9530 Epoch 1054/1200, Train Loss: 0.0453, Train Accuracy: 0.9879, Val Loss: 0.1262, Val Accuracy: 0.9530 Epoch 1055/1200, Train Loss: 0.0449, Train Accuracy: 0.9879, Val Loss: 0.1262, Val Accuracy: 0.9530 Epoch 1056/1200, Train Loss: 0.0452, Train Accuracy: 0.9868, Val Loss: 0.1261, Val Accuracy: 0.9530 Epoch 1057/1200, Train Loss: 0.0439, Train Accuracy: 0.9877, Val Loss: 0.1263, Val Accuracy: 0.9530 Epoch 1058/1200, Train Loss: 0.0437, Train Accuracy: 0.9883, Val Loss: 0.1263, Val Accuracy: 0.9530 Epoch 1059/1200, Train Loss: 0.0430, Train Accuracy: 0.9886, Val Loss: 0.1262, Val Accuracy: 0.9528 Epoch 1060/1200, Train Loss: 0.0441, Train Accuracy: 0.9880, Val Loss: 0.1264, Val Accuracy: 0.9528 Epoch 1061/1200, Train Loss: 0.0442, Train Accuracy: 0.9885, Val Loss: 0.1262, Val Accuracy: 0.9528 Epoch 1062/1200, Train Loss: 0.0418, Train Accuracy: 0.9889, Val Loss: 0.1263, Val Accuracy: 0.9530 Epoch 1063/1200, Train Loss: 0.0438, Train Accuracy: 0.9877, Val Loss: 0.1263, Val Accuracy: 0.9530 Epoch 1064/1200, Train Loss: 0.0436, Train Accuracy: 0.9880, Val Loss: 0.1264, Val Accuracy: 0.9530 Epoch 1065/1200, Train Loss: 0.0433, Train Accuracy: 0.9882, Val Loss: 0.1263, Val Accuracy: 0.9528 Epoch 1066/1200, Train Loss: 0.0437, Train Accuracy: 0.9878, Val Loss: 0.1263, Val Accuracy: 0.9526 Epoch 1067/1200, Train Loss: 0.0432, Train Accuracy: 0.9879, Val Loss: 0.1262, Val Accuracy: 0.9530 Epoch 1068/1200, Train Loss: 0.0437, Train Accuracy: 0.9876, Val Loss: 0.1262, Val Accuracy: 0.9530 Epoch 1069/1200, Train Loss: 0.0422, Train Accuracy: 0.9890, Val Loss: 0.1262, Val Accuracy: 0.9531 Epoch 1070/1200, Train Loss: 0.0435, Train Accuracy: 0.9875, Val Loss: 0.1262, Val Accuracy: 0.9531 Epoch 1071/1200, Train Loss: 0.0420, Train Accuracy: 0.9891, Val Loss: 0.1263, Val Accuracy: 0.9531 Epoch 1072/1200, Train Loss: 0.0425, Train Accuracy: 0.9886, Val Loss: 0.1262, Val Accuracy: 0.9531 Epoch 1073/1200, Train Loss: 0.0425, Train Accuracy: 0.9889, Val Loss: 0.1263, Val Accuracy: 0.9531 Epoch 1074/1200, Train Loss: 0.0428, Train Accuracy: 0.9884, Val Loss: 0.1266, Val Accuracy: 0.9531 Epoch 1075/1200, Train Loss: 0.0422, Train Accuracy: 0.9888, Val Loss: 0.1264, Val Accuracy: 0.9531 Epoch 1076/1200, Train Loss: 0.0419, Train Accuracy: 0.9888, Val Loss: 0.1265, Val Accuracy: 0.9531 Epoch 1077/1200, Train Loss: 0.0429, Train Accuracy: 0.9883, Val Loss: 0.1267, Val Accuracy: 0.9533 Epoch 1078/1200, Train Loss: 0.0425, Train Accuracy: 0.9881, Val Loss: 0.1267, Val Accuracy: 0.9531 Epoch 1079/1200, Train Loss: 0.0413, Train Accuracy: 0.9893, Val Loss: 0.1268, Val Accuracy: 0.9531 Epoch 1080/1200, Train Loss: 0.0414, Train Accuracy: 0.9880, Val Loss: 0.1267, Val Accuracy: 0.9533 Epoch 1081/1200, Train Loss: 0.0424, Train Accuracy: 0.9879, Val Loss: 0.1267, Val Accuracy: 0.9533 Epoch 1082/1200, Train Loss: 0.0416, Train Accuracy: 0.9887, Val Loss: 0.1266, Val Accuracy: 0.9533 Epoch 1083/1200, Train Loss: 0.0416, Train Accuracy: 0.9882, Val Loss: 0.1266, Val Accuracy: 0.9531 Epoch 1084/1200, Train Loss: 0.0425, Train Accuracy: 0.9883, Val Loss: 0.1264, Val Accuracy: 0.9530 Epoch 1085/1200, Train Loss: 0.0411, Train Accuracy: 0.9889, Val Loss: 0.1264, Val Accuracy: 0.9528 Epoch 1086/1200, Train Loss: 0.0415, Train Accuracy: 0.9889, Val Loss: 0.1266, Val Accuracy: 0.9530 Epoch 1087/1200, Train Loss: 0.0411, Train Accuracy: 0.9893, Val Loss: 0.1267, Val Accuracy: 0.9530 Epoch 1088/1200, Train Loss: 0.0407, Train Accuracy: 0.9896, Val Loss: 0.1265, Val Accuracy: 0.9530 Epoch 1089/1200, Train Loss: 0.0404, Train Accuracy: 0.9895, Val Loss: 0.1267, Val Accuracy: 0.9530 Epoch 1090/1200, Train Loss: 0.0409, Train Accuracy: 0.9894, Val Loss: 0.1268, Val Accuracy: 0.9530 Epoch 1091/1200, Train Loss: 0.0403, Train Accuracy: 0.9890, Val Loss: 0.1270, Val Accuracy: 0.9531 Epoch 1092/1200, Train Loss: 0.0400, Train Accuracy: 0.9896, Val Loss: 0.1269, Val Accuracy: 0.9531 Epoch 1093/1200, Train Loss: 0.0422, Train Accuracy: 0.9878, Val Loss: 0.1271, Val Accuracy: 0.9531 Epoch 1094/1200, Train Loss: 0.0407, Train Accuracy: 0.9886, Val Loss: 0.1270, Val Accuracy: 0.9531 Epoch 1095/1200, Train Loss: 0.0411, Train Accuracy: 0.9895, Val Loss: 0.1271, Val Accuracy: 0.9531 Epoch 1096/1200, Train Loss: 0.0399, Train Accuracy: 0.9883, Val Loss: 0.1270, Val Accuracy: 0.9533 Epoch 1097/1200, Train Loss: 0.0402, Train Accuracy: 0.9894, Val Loss: 0.1270, Val Accuracy: 0.9533 Epoch 1098/1200, Train Loss: 0.0408, Train Accuracy: 0.9885, Val Loss: 0.1273, Val Accuracy: 0.9531 Epoch 1099/1200, Train Loss: 0.0404, Train Accuracy: 0.9895, Val Loss: 0.1271, Val Accuracy: 0.9533 Epoch 1100/1200, Train Loss: 0.0398, Train Accuracy: 0.9892, Val Loss: 0.1271, Val Accuracy: 0.9531 Epoch 1101/1200, Train Loss: 0.0384, Train Accuracy: 0.9913, Val Loss: 0.1270, Val Accuracy: 0.9530 Epoch 1102/1200, Train Loss: 0.0400, Train Accuracy: 0.9892, Val Loss: 0.1273, Val Accuracy: 0.9531 Epoch 1103/1200, Train Loss: 0.0408, Train Accuracy: 0.9888, Val Loss: 0.1273, Val Accuracy: 0.9531 Epoch 1104/1200, Train Loss: 0.0410, Train Accuracy: 0.9890, Val Loss: 0.1272, Val Accuracy: 0.9533 Epoch 1105/1200, Train Loss: 0.0403, Train Accuracy: 0.9897, Val Loss: 0.1270, Val Accuracy: 0.9530 Epoch 1106/1200, Train Loss: 0.0393, Train Accuracy: 0.9900, Val Loss: 0.1272, Val Accuracy: 0.9531 Epoch 1107/1200, Train Loss: 0.0390, Train Accuracy: 0.9899, Val Loss: 0.1271, Val Accuracy: 0.9530 Epoch 1108/1200, Train Loss: 0.0374, Train Accuracy: 0.9900, Val Loss: 0.1270, Val Accuracy: 0.9528 Epoch 1109/1200, Train Loss: 0.0377, Train Accuracy: 0.9906, Val Loss: 0.1272, Val Accuracy: 0.9530 Epoch 1110/1200, Train Loss: 0.0393, Train Accuracy: 0.9891, Val Loss: 0.1272, Val Accuracy: 0.9530 Epoch 1111/1200, Train Loss: 0.0391, Train Accuracy: 0.9900, Val Loss: 0.1274, Val Accuracy: 0.9530 Epoch 1112/1200, Train Loss: 0.0368, Train Accuracy: 0.9915, Val Loss: 0.1274, Val Accuracy: 0.9531 Epoch 1113/1200, Train Loss: 0.0382, Train Accuracy: 0.9904, Val Loss: 0.1273, Val Accuracy: 0.9530 Epoch 1114/1200, Train Loss: 0.0382, Train Accuracy: 0.9896, Val Loss: 0.1275, Val Accuracy: 0.9530 Epoch 1115/1200, Train Loss: 0.0387, Train Accuracy: 0.9899, Val Loss: 0.1274, Val Accuracy: 0.9530 Epoch 1116/1200, Train Loss: 0.0385, Train Accuracy: 0.9907, Val Loss: 0.1275, Val Accuracy: 0.9531 Epoch 1117/1200, Train Loss: 0.0386, Train Accuracy: 0.9905, Val Loss: 0.1277, Val Accuracy: 0.9531 Epoch 1118/1200, Train Loss: 0.0390, Train Accuracy: 0.9894, Val Loss: 0.1277, Val Accuracy: 0.9531 Epoch 1119/1200, Train Loss: 0.0376, Train Accuracy: 0.9904, Val Loss: 0.1278, Val Accuracy: 0.9531 Epoch 1120/1200, Train Loss: 0.0375, Train Accuracy: 0.9901, Val Loss: 0.1276, Val Accuracy: 0.9531 Epoch 1121/1200, Train Loss: 0.0379, Train Accuracy: 0.9895, Val Loss: 0.1276, Val Accuracy: 0.9531 Epoch 1122/1200, Train Loss: 0.0384, Train Accuracy: 0.9905, Val Loss: 0.1277, Val Accuracy: 0.9531 Epoch 1123/1200, Train Loss: 0.0382, Train Accuracy: 0.9895, Val Loss: 0.1276, Val Accuracy: 0.9530 Epoch 1124/1200, Train Loss: 0.0367, Train Accuracy: 0.9909, Val Loss: 0.1275, Val Accuracy: 0.9530 Epoch 1125/1200, Train Loss: 0.0382, Train Accuracy: 0.9902, Val Loss: 0.1276, Val Accuracy: 0.9531 Epoch 1126/1200, Train Loss: 0.0368, Train Accuracy: 0.9906, Val Loss: 0.1276, Val Accuracy: 0.9533 Epoch 1127/1200, Train Loss: 0.0383, Train Accuracy: 0.9900, Val Loss: 0.1278, Val Accuracy: 0.9531 Epoch 1128/1200, Train Loss: 0.0384, Train Accuracy: 0.9895, Val Loss: 0.1276, Val Accuracy: 0.9526 Epoch 1129/1200, Train Loss: 0.0369, Train Accuracy: 0.9902, Val Loss: 0.1280, Val Accuracy: 0.9531 Epoch 1130/1200, Train Loss: 0.0371, Train Accuracy: 0.9904, Val Loss: 0.1279, Val Accuracy: 0.9531 Epoch 1131/1200, Train Loss: 0.0366, Train Accuracy: 0.9900, Val Loss: 0.1279, Val Accuracy: 0.9531 Epoch 1132/1200, Train Loss: 0.0368, Train Accuracy: 0.9905, Val Loss: 0.1278, Val Accuracy: 0.9530 Epoch 1133/1200, Train Loss: 0.0374, Train Accuracy: 0.9897, Val Loss: 0.1277, Val Accuracy: 0.9524 Epoch 1134/1200, Train Loss: 0.0360, Train Accuracy: 0.9911, Val Loss: 0.1276, Val Accuracy: 0.9524 Epoch 1135/1200, Train Loss: 0.0359, Train Accuracy: 0.9905, Val Loss: 0.1277, Val Accuracy: 0.9524 Epoch 1136/1200, Train Loss: 0.0363, Train Accuracy: 0.9901, Val Loss: 0.1278, Val Accuracy: 0.9526 Epoch 1137/1200, Train Loss: 0.0369, Train Accuracy: 0.9898, Val Loss: 0.1282, Val Accuracy: 0.9530 Epoch 1138/1200, Train Loss: 0.0362, Train Accuracy: 0.9909, Val Loss: 0.1280, Val Accuracy: 0.9530 Epoch 1139/1200, Train Loss: 0.0358, Train Accuracy: 0.9912, Val Loss: 0.1281, Val Accuracy: 0.9530 Epoch 1140/1200, Train Loss: 0.0353, Train Accuracy: 0.9907, Val Loss: 0.1281, Val Accuracy: 0.9530 Epoch 1141/1200, Train Loss: 0.0367, Train Accuracy: 0.9903, Val Loss: 0.1281, Val Accuracy: 0.9530 Epoch 1142/1200, Train Loss: 0.0343, Train Accuracy: 0.9921, Val Loss: 0.1281, Val Accuracy: 0.9526 Epoch 1143/1200, Train Loss: 0.0342, Train Accuracy: 0.9913, Val Loss: 0.1280, Val Accuracy: 0.9526 Epoch 1144/1200, Train Loss: 0.0361, Train Accuracy: 0.9911, Val Loss: 0.1281, Val Accuracy: 0.9526 Epoch 1145/1200, Train Loss: 0.0359, Train Accuracy: 0.9914, Val Loss: 0.1282, Val Accuracy: 0.9526 Epoch 1146/1200, Train Loss: 0.0356, Train Accuracy: 0.9915, Val Loss: 0.1284, Val Accuracy: 0.9526 Epoch 1147/1200, Train Loss: 0.0345, Train Accuracy: 0.9921, Val Loss: 0.1281, Val Accuracy: 0.9526 Epoch 1148/1200, Train Loss: 0.0354, Train Accuracy: 0.9908, Val Loss: 0.1282, Val Accuracy: 0.9524 Epoch 1149/1200, Train Loss: 0.0354, Train Accuracy: 0.9909, Val Loss: 0.1282, Val Accuracy: 0.9526 Epoch 1150/1200, Train Loss: 0.0347, Train Accuracy: 0.9916, Val Loss: 0.1284, Val Accuracy: 0.9526 Epoch 1151/1200, Train Loss: 0.0348, Train Accuracy: 0.9912, Val Loss: 0.1284, Val Accuracy: 0.9526 Epoch 1152/1200, Train Loss: 0.0350, Train Accuracy: 0.9908, Val Loss: 0.1286, Val Accuracy: 0.9526 Epoch 1153/1200, Train Loss: 0.0343, Train Accuracy: 0.9914, Val Loss: 0.1285, Val Accuracy: 0.9524 Epoch 1154/1200, Train Loss: 0.0352, Train Accuracy: 0.9908, Val Loss: 0.1285, Val Accuracy: 0.9524 Epoch 1155/1200, Train Loss: 0.0339, Train Accuracy: 0.9915, Val Loss: 0.1288, Val Accuracy: 0.9528 Epoch 1156/1200, Train Loss: 0.0344, Train Accuracy: 0.9914, Val Loss: 0.1286, Val Accuracy: 0.9522 Epoch 1157/1200, Train Loss: 0.0346, Train Accuracy: 0.9918, Val Loss: 0.1287, Val Accuracy: 0.9524 Epoch 1158/1200, Train Loss: 0.0351, Train Accuracy: 0.9909, Val Loss: 0.1285, Val Accuracy: 0.9524 Epoch 1159/1200, Train Loss: 0.0345, Train Accuracy: 0.9912, Val Loss: 0.1287, Val Accuracy: 0.9522 Epoch 1160/1200, Train Loss: 0.0337, Train Accuracy: 0.9918, Val Loss: 0.1289, Val Accuracy: 0.9524 Epoch 1161/1200, Train Loss: 0.0337, Train Accuracy: 0.9916, Val Loss: 0.1288, Val Accuracy: 0.9520 Epoch 1162/1200, Train Loss: 0.0348, Train Accuracy: 0.9915, Val Loss: 0.1288, Val Accuracy: 0.9520 Epoch 1163/1200, Train Loss: 0.0348, Train Accuracy: 0.9911, Val Loss: 0.1291, Val Accuracy: 0.9528 Epoch 1164/1200, Train Loss: 0.0347, Train Accuracy: 0.9913, Val Loss: 0.1291, Val Accuracy: 0.9526 Epoch 1165/1200, Train Loss: 0.0333, Train Accuracy: 0.9915, Val Loss: 0.1290, Val Accuracy: 0.9522 Epoch 1166/1200, Train Loss: 0.0341, Train Accuracy: 0.9912, Val Loss: 0.1291, Val Accuracy: 0.9522 Epoch 1167/1200, Train Loss: 0.0336, Train Accuracy: 0.9916, Val Loss: 0.1291, Val Accuracy: 0.9520 Epoch 1168/1200, Train Loss: 0.0336, Train Accuracy: 0.9921, Val Loss: 0.1291, Val Accuracy: 0.9522 Epoch 1169/1200, Train Loss: 0.0332, Train Accuracy: 0.9920, Val Loss: 0.1289, Val Accuracy: 0.9522 Epoch 1170/1200, Train Loss: 0.0342, Train Accuracy: 0.9909, Val Loss: 0.1288, Val Accuracy: 0.9524 Epoch 1171/1200, Train Loss: 0.0332, Train Accuracy: 0.9919, Val Loss: 0.1290, Val Accuracy: 0.9520 Epoch 1172/1200, Train Loss: 0.0322, Train Accuracy: 0.9925, Val Loss: 0.1291, Val Accuracy: 0.9520 Epoch 1173/1200, Train Loss: 0.0339, Train Accuracy: 0.9914, Val Loss: 0.1292, Val Accuracy: 0.9522 Epoch 1174/1200, Train Loss: 0.0323, Train Accuracy: 0.9923, Val Loss: 0.1291, Val Accuracy: 0.9520 Epoch 1175/1200, Train Loss: 0.0327, Train Accuracy: 0.9917, Val Loss: 0.1292, Val Accuracy: 0.9520 Epoch 1176/1200, Train Loss: 0.0322, Train Accuracy: 0.9925, Val Loss: 0.1294, Val Accuracy: 0.9520 Epoch 1177/1200, Train Loss: 0.0323, Train Accuracy: 0.9925, Val Loss: 0.1295, Val Accuracy: 0.9520 Epoch 1178/1200, Train Loss: 0.0343, Train Accuracy: 0.9914, Val Loss: 0.1293, Val Accuracy: 0.9520 Epoch 1179/1200, Train Loss: 0.0330, Train Accuracy: 0.9918, Val Loss: 0.1294, Val Accuracy: 0.9520 Epoch 1180/1200, Train Loss: 0.0324, Train Accuracy: 0.9914, Val Loss: 0.1295, Val Accuracy: 0.9520 Epoch 1181/1200, Train Loss: 0.0325, Train Accuracy: 0.9914, Val Loss: 0.1294, Val Accuracy: 0.9520 Epoch 1182/1200, Train Loss: 0.0334, Train Accuracy: 0.9911, Val Loss: 0.1295, Val Accuracy: 0.9520 Epoch 1183/1200, Train Loss: 0.0331, Train Accuracy: 0.9918, Val Loss: 0.1295, Val Accuracy: 0.9520 Epoch 1184/1200, Train Loss: 0.0325, Train Accuracy: 0.9921, Val Loss: 0.1298, Val Accuracy: 0.9522 Epoch 1185/1200, Train Loss: 0.0319, Train Accuracy: 0.9917, Val Loss: 0.1297, Val Accuracy: 0.9520 Epoch 1186/1200, Train Loss: 0.0320, Train Accuracy: 0.9923, Val Loss: 0.1297, Val Accuracy: 0.9520 Epoch 1187/1200, Train Loss: 0.0317, Train Accuracy: 0.9929, Val Loss: 0.1298, Val Accuracy: 0.9520 Epoch 1188/1200, Train Loss: 0.0319, Train Accuracy: 0.9923, Val Loss: 0.1298, Val Accuracy: 0.9520 Epoch 1189/1200, Train Loss: 0.0314, Train Accuracy: 0.9927, Val Loss: 0.1296, Val Accuracy: 0.9520 Epoch 1190/1200, Train Loss: 0.0324, Train Accuracy: 0.9918, Val Loss: 0.1295, Val Accuracy: 0.9520 Epoch 1191/1200, Train Loss: 0.0303, Train Accuracy: 0.9932, Val Loss: 0.1296, Val Accuracy: 0.9520 Epoch 1192/1200, Train Loss: 0.0317, Train Accuracy: 0.9922, Val Loss: 0.1297, Val Accuracy: 0.9522 Epoch 1193/1200, Train Loss: 0.0314, Train Accuracy: 0.9930, Val Loss: 0.1297, Val Accuracy: 0.9522 Epoch 1194/1200, Train Loss: 0.0314, Train Accuracy: 0.9922, Val Loss: 0.1296, Val Accuracy: 0.9520 Epoch 1195/1200, Train Loss: 0.0308, Train Accuracy: 0.9934, Val Loss: 0.1297, Val Accuracy: 0.9520 Epoch 1196/1200, Train Loss: 0.0324, Train Accuracy: 0.9913, Val Loss: 0.1299, Val Accuracy: 0.9522 Epoch 1197/1200, Train Loss: 0.0311, Train Accuracy: 0.9924, Val Loss: 0.1299, Val Accuracy: 0.9520 Epoch 1198/1200, Train Loss: 0.0304, Train Accuracy: 0.9932, Val Loss: 0.1299, Val Accuracy: 0.9522 Epoch 1199/1200, Train Loss: 0.0308, Train Accuracy: 0.9925, Val Loss: 0.1304, Val Accuracy: 0.9518 Epoch 1200/1200, Train Loss: 0.0304, Train Accuracy: 0.9926, Val Loss: 0.1302, Val Accuracy: 0.9520
torch.save(model3.state_dict(), best_model_path3)
from sklearn.model_selection import GridSearchCV
from sklearn.neural_network import MLPClassifier
# Create the neural network classifier
clf = MLPClassifier(random_state=42)
# Assuming you have performed a grid search and obtained the best estimator
# Define the parameter grid
param_grid = {
'hidden_layer_sizes': [(30,30), (10,10,10)],
'solver': ['adam', 'sgd'],
'alpha': [0.001, 0.01],
}
# Create the GridSearchCV object
grid_search = GridSearchCV(clf, param_grid, scoring='accuracy', cv=2)
# Fit the GridSearchCV object to the data
grid_search.fit(X_train_all_nn, y_train_all_nn)
# Save the best estimator in best_nn
best_nn_all = grid_search.best_estimator_
print(best_nn_all)
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn(
MLPClassifier(alpha=0.001, hidden_layer_sizes=(30, 30), random_state=42,
solver='sgd')
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn(
¶
Test for the model all datasets merged
# Load the best model
model3.load_state_dict(torch.load('best_model_final3.pth'))
# Evaluate on test data
model3.eval()
X_test_all_tensor2 = torch.tensor(X_test_all.values, dtype=torch.float32)
y_test_all_tensor2 = torch.tensor(y_test_all.values, dtype=torch.float32).unsqueeze(1)
with torch.no_grad():
test_output = model3(X_test_all_tensor2)
test_loss = criterion(test_output, y_test_all_tensor2)
test_predictions = torch.round(torch.sigmoid(test_output))
test_correct = (test_predictions == y_test_all_tensor2).sum().item()
test_total = y_test_all_tensor2.size(0)
test_acc = test_correct / test_total
print(f'Test Loss: {test_loss.item():.4f}, Test Accuracy: {test_acc:.4f}')
Test Loss: 0.1287, Test Accuracy: 0.9565
¶
4.6.5) KNN
best_knn_all = train_and_evaluate_knn(X_train_all, X_test_all, y_train_all, y_test_all)
Best hyperparameters: {'n_neighbors': 2}
Accuracy: 0.6018871348212665
F1: 0.5387849484969519
Classification Report:
precision recall f1-score support
0 0.58 0.74 0.65 5537
1 0.64 0.47 0.54 5485
accuracy 0.60 11022
macro avg 0.61 0.60 0.59 11022
weighted avg 0.61 0.60 0.59 11022
¶
4.6.6) Logistic Regression
best_logreg_all = train_and_evaluate_logreg(X_train_all, X_test_all, y_train_all, y_test_all)
Best hyperparameters: {'C': 0.5, 'penalty': 'l2'}
Accuracy: 0.9505534385773907
F1: 0.949866617606476
Classification Report:
precision recall f1-score support
0 0.94 0.96 0.95 5537
1 0.96 0.94 0.95 5485
accuracy 0.95 11022
macro avg 0.95 0.95 0.95 11022
weighted avg 0.95 0.95 0.95 11022
best_catboost_all=train_and_evaluate_catboost(X_train_all, X_test_all, y_train_all, y_test_all)
Best hyperparameters: {'logging_level': 'Silent'}
Accuracy: 0.9682453275267646
F1: 0.9678957989359751
Classification Report:
precision recall f1-score support
0 0.96 0.97 0.97 5537
1 0.97 0.96 0.97 5485
accuracy 0.97 11022
macro avg 0.97 0.97 0.97 11022
weighted avg 0.97 0.97 0.97 11022
¶
5 ) Voting Classifiers
Finally, we decided to join the 4 different models into a general one using as ensamble learning model.
To be consistent with the split of training and test data on which we should then have evaluated the performance of the model, we decided to join separately all the train and test split of the four datasets in order to build two big and completely independent training and test datasets.
#now i want to build a voting classifier with the 4 best models
#before doing that i need to build the train and test dataset joining the ones i used for the models
#i will use the minmaxscaler to rescale the data
scaler = MinMaxScaler()
X_train_df_SS_MCF7.iloc[ : , :] = scaler.fit_transform(X_train_df_SS_MCF7)
X_test_df_SS_MCF7.iloc[ : , :] = scaler.fit_transform(X_test_df_SS_MCF7)
X_train_df_SS_HCC1806.iloc[ : , :] = scaler.fit_transform(X_train_df_SS_HCC1806)
X_test_df_SS_HCC1806.iloc[ : , :] = scaler.fit_transform(X_test_df_SS_HCC1806)
X_train_df_DS_MCF7.iloc[ : , :] = scaler.fit_transform(X_train_df_DS_MCF7)
X_test_df_DS_MCF7.iloc[ : , :] = scaler.fit_transform(X_test_df_DS_MCF7)
X_train_df_DS_HCC1806.iloc[ : , :] = scaler.fit_transform(X_train_df_DS_HCC1806)
X_test_df_DS_HCC1806.iloc[ : , :] = scaler.fit_transform(X_test_df_DS_HCC1806)
X_test = pd.concat([X_test_df_SS_MCF7, X_test_df_SS_HCC1806, X_test_df_DS_MCF7, X_test_df_DS_HCC1806])
X_train = pd.concat([X_train_df_SS_MCF7, X_train_df_SS_HCC1806, X_train_df_DS_MCF7, X_train_df_DS_HCC1806])
#i substitute the NaN values with 0
X_test.fillna(0, inplace=True)
X_train.fillna(0, inplace=True)
y_train = pd.concat([y_train_df_SS_MCF7, y_train_df_SS_HCC1806, y_train_df_DS_MCF7, y_train_df_DS_HCC1806])
y_test = pd.concat([y_test_df_SS_MCF7, y_test_df_SS_HCC1806, y_test_df_DS_MCF7, y_test_df_DS_HCC1806])
In order to join the four predictors, we decide to use a Voting classifier.
It is a powerful ensemble learning technique that combines the predictions of multiple classifiers to make more accurate and robust predictions.
We decide to use the 'soft' voting approach: in soft voting, the final prediction is the class with the highest average probability predicted by the base classifiers. For classifiers that output class probabilities, the probabilities are averaged across all base classifiers, and the class with the highest average probability is chosen as the final prediction.
¶
5.1) SVM
from sklearn.ensemble import VotingClassifier
# Create the voting classifier
voting_clf_SVM = VotingClassifier(
estimators=[
('svm ds hcc', best_svm_DS_HCC1806),
('svm ds mcf7', best_svm_DS_MCF7),
('svm ss hcc', best_svm_SS_HCC1806),
('svm ss mcf7', best_svm_SS_MCF7),
],
voting='hard' # Use majority voting
)
# Fit the voting classifier on the training data
voting_clf_SVM.fit(X_train, y_train)
# Evaluate the voting classifier on the test data
test_accuracy = voting_clf_SVM.score(X_test, y_test)
print("Test accuracy: ", test_accuracy)
Test accuracy: 0.9471842907121092
¶
5.2) XGB
#ensamble the models
# Define the voting classifier
voting_clf_XGB = VotingClassifier(estimators=[('best_rf_SS_MCF7', best_rf_SS_MCF7), ('best_xgb_SS_HCC1806', best_xgb_SS_HCC1806),
('best_xgb_DS_HCC1806', best_xgb_DS_HCC1806), ('best_xgb_DS_MCF7', best_xgb_DS_MCF7)], voting='soft')
outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)
# Perform the cross validation
scores = cross_val_score(voting_clf_XGB, X_train, y_train, cv=outer_cv)
# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))
# Fit the voting classifier on the train dataset
voting_clf_XGB.fit(X_train, y_train)
#print the accuracy of the model
print("Accuracy of the model: ", voting_clf_XGB.score(X_test, y_test))
Mean accuracy of nested cross validation: 0.9599875973291393 Accuracy of the model: 0.9545495781547673
As a final consideration we can say that the general xgboost model had a better performance in the test set with respect to the Voting classifer made up of the 4 singluar preditors, but since the difference is minimal (only 0.5%), for the prediction on a completely new dataset we would recomment to still use both to also have a comparison of the effectiveness when they are supposed to predict the target of completely unseen data in which features could have different properties from the ones found in our datasets.
¶
5.3) KNN
# Create the voting classifier
voting_clf_knn = VotingClassifier(
estimators=[
('knn ds hcc', best_knn_DS_HCC1806),
('knn ds mcf7', best_knn_DS_MCF7),
('knn ss hcc', best_knn_SS_HCC1806),
('knn ss mcf7', best_knn_DS_MCF7),
],
voting='hard' # Use majority voting
)
# Fit the voting classifier on the training data
voting_clf_knn.fit(X_train, y_train)
# Evaluate the voting classifier on the test data
test_accuracy = voting_clf_knn.score(X_test, y_test)
print("Test accuracy: ", test_accuracy)
Test accuracy: 0.5579505699131023
¶
5.4) Logistic Regression
# Create the voting classifier
voting_clf_logreg = VotingClassifier(
estimators=[
('logreg ds hcc', best_logreg_DS_HCC1806),
('logreg ds mcf7', best_logreg_DS_MCF7),
('logreg ss hcc', best_logreg_SS_HCC1806),
('logreg ss mcf7', best_logreg_DS_MCF7),
],
voting='hard' # Use majority voting
)
# Fit the voting classifier on the training data
voting_clf_logreg.fit(X_train, y_train)
# Evaluate the voting classifier on the test data
test_accuracy = voting_clf_logreg.score(X_test, y_test)
print("Test accuracy: ", test_accuracy)
Test accuracy: 0.8599480871233495
# Create the voting classifier
voting_clf_logreg2 = VotingClassifier(
estimators=[
('logreg2 ss mcf', model_logreg_mc_SMART_ver2_SS_MCF),
('logreg2 ss hcc', model_logreg_hcc_SMART_ver2_SS_HCC),
('logreg2 ds mcf', model_logreg_mc_DROP_ver2_DS_MCF),
('logreg2 ds hcc', model_logreg_hcc_DROP_ver2_DS_HCC),
],
voting='hard' # Use majority voting
)
# Fit the voting classifier on the training data
voting_clf_logreg2.fit(X_train, y_train)
# Evaluate the voting classifier on the test data
test_accuracy = voting_clf_logreg2.score(X_test, y_test)
print("Test accuracy: ", test_accuracy)
Test accuracy: 0.8599480871233495
¶
5.5) catboost
# Create the voting classifier
voting_clf_catboost = VotingClassifier(
estimators=[
('catboost ds hcc', best_catboost_DS_HCC1806),
('catboost ds mcf7', best_catboost_DS_MCF7),
('catboost ss hcc', best_catboost_SS_HCC1806),
('catboost ss mcf7', best_catboost_DS_MCF7),
],
voting='hard' # Use majority voting
)
# Fit the voting classifier on the training data
voting_clf_catboost.fit(X_train, y_train)
# Evaluate the voting classifier on the test data
test_accuracy = voting_clf_catboost.score(X_test, y_test)
print("Test accuracy: ", test_accuracy)
Test accuracy: 0.960049655795057
# Create the voting classifier
voting_clf_catboost2 = VotingClassifier(
estimators=[
('catboost ss mcf', model_catboost_mc_SMART_ver2_SS_MCF),
('catboost ss hcc', model_catboost_hcc_SMART_ver2_SS_HCC),
('catboost ds mcf', model_catboost_mc_DROP_ver2_DS_MCF),
('catboost ds hcc', model_catboost_hcc_DROP_ver2_DS_HCC),
],
voting='hard' # Use majority voting
)
# Fit the voting classifier on the training data
voting_clf_catboost2.fit(X_train, y_train)
# Evaluate the voting classifier on the test data
test_accuracy = voting_clf_catboost2.score(X_test, y_test)
print("Test accuracy: ", test_accuracy)
Test accuracy: 0.9535041191739081
¶
6 ) Predictions
Now we want to make prediction. For each dataset we use the 3 best models made before.
So, for each dataset we watch to predictors with highest test accuracy and to put 0 or 1 for prediction "x" we take the mode value for it (ex: if there are two 0 and one 1, we have 0). For this purpose we use an odd number of predictors.
¶
6.1) Prediction on SmartSeq MCF7
¶
6.1.0) Loading dataset Start by opening the dataset "MCF7_SmartS_Filtered_Normalised_3000_Data_test_anonim"
df_SS_MCF7_anon = pd.read_csv("SmartSeq/MCF7_SmartS_Filtered_Normalised_3000_Data_test_anonim.txt", delimiter="\ ",engine='python',index_col=0).T
print("Dataframe dimensions:", np.shape(df_SS_MCF7_anon))
Dataframe dimensions: (63, 3000)
¶
6.1.1) Random Forest
predictions_SS_MCF7_rf = best_rf_SS_MCF7.predict(df_SS_MCF7_anon)
# Convert predictions to a DataFrame and save as CSV
predictions_SS_MCF7_rf_df = pd.DataFrame(predictions_SS_MCF7_rf, columns=['Predictions'])
predictions_SS_MCF7_rf_df
| Predictions | |
|---|---|
| 0 | 1 |
| 1 | 1 |
| 2 | 0 |
| 3 | 0 |
| 4 | 0 |
| ... | ... |
| 58 | 1 |
| 59 | 1 |
| 60 | 1 |
| 61 | 0 |
| 62 | 0 |
63 rows × 1 columns
¶
6.1.2) Logistic Regression
predictions_SS_MCF7_logreg = best_logreg_SS_MCF7.predict(df_SS_MCF7_anon)
predictions_SS_MCF7_logreg
array([1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1,
0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0])
# Convert predictions to a DataFrame and save as CSV
predictions_SS_MCF7_logreg_df = pd.DataFrame(predictions_SS_MCF7_logreg, columns=['Predictions'])
predictions_SS_MCF7_logreg_df
| Predictions | |
|---|---|
| 0 | 1 |
| 1 | 1 |
| 2 | 0 |
| 3 | 0 |
| 4 | 0 |
| ... | ... |
| 58 | 1 |
| 59 | 1 |
| 60 | 1 |
| 61 | 0 |
| 62 | 0 |
63 rows × 1 columns
¶
6.1.3)SVM
predictions_SS_MCF7_svm = best_svm_SS_MCF7.predict(df_SS_MCF7_anon)
# Convert predictions to a DataFrame and save as CSV
predictions_SS_MCF7_svm = pd.DataFrame(predictions_SS_MCF7_svm, columns=['Predictions'])
predictions_SS_MCF7_svm_array=np.array(predictions_SS_MCF7_svm)
predictions_SS_MCF7_svm_array
array([[1],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[1],
[0],
[0],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[1],
[0],
[0],
[0],
[0],
[1],
[1],
[0],
[1],
[1],
[1],
[1],
[0],
[0],
[1],
[1],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[0],
[0]])
¶
6.1.4) FINAL CONSIDERATION
k=0
for i in range(63):
if predictions_SS_MCF7_rf[i]!=predictions_SS_MCF7_logreg[i]:
k=k+1
print("LOGISTIC VS RF", "\nnumber errors: ", k, "\npercentage correspondance: ", (63-k)/63)
LOGISTIC VS RF number errors: 0 percentage correspondance: 1.0
k=0
for i in range(63):
if predictions_SS_MCF7_svm_array[i] != predictions_SS_MCF7_logreg[i]:
k=k+1
print("LOGISTIC VS SVM", "\nnumber errors: ", k, "\npercentage correspondance: ", (63-k)/63)
LOGISTIC VS SVM number errors: 0 percentage correspondance: 1.0
k=0
for i in range(63):
if predictions_SS_MCF7_rf[i]!=predictions_SS_MCF7_svm_array[i]:
k=k+1
print("SVM VS RF", "\nnumber errors: ", k, "\npercentage correspondance: ", (63-k)/63)
SVM VS RF number errors: 0 percentage correspondance: 1.0
from scipy import stats
# Assuming predictions_SS_MCF7_rf, predictions_SS_MCF7_logreg, and predictions_SS_MCF7_svm_array are numpy arrays
predictions_SS_MCF7_rf = np.array(predictions_SS_MCF7_rf)
predictions_SS_MCF7_logreg = np.array(predictions_SS_MCF7_logreg)
predictions_SS_MCF7_svm_array = np.array(predictions_SS_MCF7_svm_array.T)
# Stack the predictions to create a 2D array (each row corresponds to the predictions for one instance)
stacked_predictions = np.vstack((predictions_SS_MCF7_rf, predictions_SS_MCF7_logreg, predictions_SS_MCF7_svm_array))
# Compute the mode along the columns
final_prediction_SS_MCF7, _ = stats.mode(stacked_predictions, axis=0)
# Since stats.mode returns a 2D array, we flatten it to get a 1D array
final_prediction_SS_MCF7 = final_prediction_SS_MCF7.flatten()
final_prediction_SS_MCF7
array([1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1,
0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
dtype=int64)
final_prediction_SS_MCF7_df = pd.DataFrame(final_prediction_SS_MCF7, columns=['Predictions'])
final_prediction_SS_MCF7_df.to_csv('final_prediction_SS_MCF7.csv', index=True)
k=0
for i in range(63):
if final_prediction_SS_MCF7[i]!=predictions_SS_MCF7_svm_array[i]:
k=k+1
print("Differences final_prediction_SS_MCF7 and SVM_SS_MCF7", k, "\npercentage correspondance final_prediction_SS_MCF7 and SVM_SS_MCF7: ", (63-k)/63)
SVM VS RF number errors: 0 percentage correspondance: 1.0
k=0
for i in range(63):
if predictions_SS_MCF7_rf[i]!=final_prediction_SS_MCF7[i]:
k=k+1
print("Differences final_prediction_SS_MCF7 and RF_SS_MCF7", k, "\npercentage correspondance final_prediction_SS_MCF7 and SRF_SS_MCF7: ", (63-k)/63)
SVM VS RF number errors: 0 percentage correspondance: 1.0
k=0
for i in range(63):
if final_prediction_SS_MCF7[i]!=predictions_SS_MCF7_logreg[i]:
k=k+1
print("Differences final_prediction_SS_MCF7 and Logistic_Regressino_SS_MCF7", k, "\npercentage correspondance final_prediction_SS_MCF7 and Logistic_Regressino_SS_MCF7: ", (63-k)/63)
SVM VS RF number errors: 0 percentage correspondance: 1.0
¶
6.2) Prediction on SmartSeq HCC1806
¶
6.2.0) Start by opening the dataset "HCC1806_SmartS_Filtered_Normalised_3000_Data_test_anonim"
df_SS_HCC1806_anon = pd.read_csv("SmartSeq/HCC1806_SmartS_Filtered_Normalised_3000_Data_test_anonim.txt",delimiter="\ ",engine='python',index_col=0).T
print("Dataframe dimensions:", np.shape(df_SS_HCC1806_anon))
Dataframe dimensions: (45, 3000)
¶
6.2.1) SVM
predictions_SS_HCC1806_svm = best_svm_SS_HCC1806.predict(df_SS_HCC1806_anon)
# Convert predictions to a DataFrame and save as CSV
predictions_SS_HCC1806_svm_df = pd.DataFrame(predictions_SS_HCC1806_svm, columns=['Predictions'])
predictions_SS_HCC1806_svm_df
| Predictions | |
|---|---|
| 0 | 0 |
| 1 | 0 |
| 2 | 1 |
| 3 | 1 |
| 4 | 1 |
| 5 | 1 |
| 6 | 1 |
| 7 | 0 |
| 8 | 0 |
| 9 | 0 |
| 10 | 0 |
| 11 | 0 |
| 12 | 1 |
| 13 | 1 |
| 14 | 1 |
| 15 | 1 |
| 16 | 0 |
| 17 | 0 |
| 18 | 0 |
| 19 | 0 |
| 20 | 0 |
| 21 | 1 |
| 22 | 1 |
| 23 | 1 |
| 24 | 0 |
| 25 | 0 |
| 26 | 0 |
| 27 | 1 |
| 28 | 0 |
| 29 | 1 |
| 30 | 1 |
| 31 | 0 |
| 32 | 0 |
| 33 | 1 |
| 34 | 1 |
| 35 | 1 |
| 36 | 0 |
| 37 | 0 |
| 38 | 0 |
| 39 | 1 |
| 40 | 1 |
| 41 | 0 |
| 42 | 1 |
| 43 | 0 |
| 44 | 0 |
¶
6.2.2) CatBoost
predictions_SS_HCC1806_catboost = best_catboost_SS_HCC1806.predict(df_SS_HCC1806_anon)
# Convert predictions to a DataFrame and save as CSV
predictions_SS_HCC1806_catboost_df = pd.DataFrame(predictions_SS_HCC1806_catboost, columns=['Predictions'])
predictions_SS_HCC1806_catboost_df
| Predictions | |
|---|---|
| 0 | 0 |
| 1 | 0 |
| 2 | 1 |
| 3 | 1 |
| 4 | 1 |
| 5 | 1 |
| 6 | 1 |
| 7 | 0 |
| 8 | 0 |
| 9 | 0 |
| 10 | 0 |
| 11 | 0 |
| 12 | 1 |
| 13 | 1 |
| 14 | 1 |
| 15 | 1 |
| 16 | 0 |
| 17 | 0 |
| 18 | 0 |
| 19 | 0 |
| 20 | 0 |
| 21 | 1 |
| 22 | 1 |
| 23 | 1 |
| 24 | 0 |
| 25 | 0 |
| 26 | 0 |
| 27 | 0 |
| 28 | 0 |
| 29 | 1 |
| 30 | 1 |
| 31 | 0 |
| 32 | 0 |
| 33 | 1 |
| 34 | 1 |
| 35 | 1 |
| 36 | 0 |
| 37 | 0 |
| 38 | 0 |
| 39 | 1 |
| 40 | 1 |
| 41 | 0 |
| 42 | 0 |
| 43 | 1 |
| 44 | 0 |
¶
6.2.3) Logistic Regression
predictions_SS_HCC1806_logreg = best_logreg_SS_HCC1806.predict(df_SS_HCC1806_anon)
predictions_SS_HCC1806_logreg_df = pd.DataFrame(predictions_SS_HCC1806_logreg, columns=['Predictions'])
predictions_SS_HCC1806_logreg_df
| Predictions | |
|---|---|
| 0 | 0 |
| 1 | 0 |
| 2 | 1 |
| 3 | 1 |
| 4 | 1 |
| 5 | 1 |
| 6 | 1 |
| 7 | 0 |
| 8 | 0 |
| 9 | 0 |
| 10 | 0 |
| 11 | 0 |
| 12 | 1 |
| 13 | 1 |
| 14 | 1 |
| 15 | 1 |
| 16 | 0 |
| 17 | 0 |
| 18 | 0 |
| 19 | 0 |
| 20 | 0 |
| 21 | 1 |
| 22 | 1 |
| 23 | 1 |
| 24 | 0 |
| 25 | 0 |
| 26 | 0 |
| 27 | 1 |
| 28 | 0 |
| 29 | 1 |
| 30 | 1 |
| 31 | 0 |
| 32 | 0 |
| 33 | 1 |
| 34 | 1 |
| 35 | 1 |
| 36 | 0 |
| 37 | 0 |
| 38 | 0 |
| 39 | 0 |
| 40 | 1 |
| 41 | 0 |
| 42 | 1 |
| 43 | 0 |
| 44 | 0 |
¶
6.2.4) Final considerations
k=0
for i in range(45):
if predictions_SS_HCC1806_svm[i]!=predictions_SS_HCC1806_catboost[i]:
k=k+1
print("SVM VS CatBoost", "\nnumber errors: ", k, "\npercentage correspondance: ", (45-k)/45)
SVM VS CatBoost number errors: 3 percentage correspondance: 0.9333333333333333
k=0
for i in range(45):
if predictions_SS_HCC1806_svm[i]!=predictions_SS_HCC1806_logreg[i]:
k=k+1
print("SVM VS Logistic", "\nnumber errors: ", k, "\npercentage correspondance: ", (45-k)/45)
SVM VS Logistic number errors: 1 percentage correspondance: 0.9777777777777777
k=0
for i in range(45):
if predictions_SS_HCC1806_catboost[i]!=predictions_SS_HCC1806_logreg[i]:
k=k+1
print("Catboost VS Logistic", "\nnumber errors: ", k, "\npercentage correspondance: ", (45-k)/45)
Catboost VS Logistic number errors: 4 percentage correspondance: 0.9111111111111111
from scipy import stats
# Stack the predictions to create a 2D array (each row corresponds to the predictions for one instance)
stacked_predictions1 = np.vstack((predictions_SS_HCC1806_svm, predictions_SS_HCC1806_logreg, predictions_SS_HCC1806_catboost))
# Compute the mode along the columns
final_prediction_SS_HCC1806, _ = stats.mode(stacked_predictions1, axis=0)
# Since stats.mode returns a 2D array, we flatten it to get a 1D array
final_prediction_SS_HCC1806 = final_prediction_SS_HCC1806.flatten()
final_prediction_SS_HCC1806
array([0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,
1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0,
0], dtype=int64)
final_prediction_SS_HCC1806_df = pd.DataFrame(final_prediction_SS_HCC1806, columns=['Predictions'])
final_prediction_SS_HCC1806_df.to_csv('final_prediction_SS_HCC1806.csv', index=True)
k=0
for i in range(45):
if final_prediction_SS_HCC1806[i]!=predictions_SS_HCC1806_catboost[i]:
k=k+1
print("Differences final_prediction_SS_HCC1806 and Catboost_SS_HCC1806", k, "\npercentage correspondance final_prediction_SS_HCC1806 and Catboost_SS_HCC1806: ", (45-k)/45)
Catboost VS Logistic number errors: 3 percentage correspondance: 0.9333333333333333
k=0
for i in range(45):
if final_prediction_SS_HCC1806[i]!=predictions_SS_HCC1806_svm[i]:
k=k+1
print("Differences final_prediction_SS_HCC1806 and SVM_SS_HCC1806", k, "\npercentage correspondance final_prediction_SS_HCC1806 and SVM_SS_HCC1806: ", (45-k)/45)
Catboost VS Logistic number errors: 3 percentage correspondance: 0.9333333333333333
k=0
for i in range(45):
if final_prediction_SS_HCC1806[i]!=predictions_SS_HCC1806_logreg[i]:
k=k+1
print("Differences final_prediction_SS_HCC1806 and LogReg_SS_HCC1806", k, "\npercentage correspondance final_prediction_SS_HCC1806 and LogReg_SS_HCC1806: ", (45-k)/45)
Catboost VS Logistic number errors: 3 percentage correspondance: 0.9333333333333333
¶
6.3) Prediction on DropSeq MCF7
¶
6.3.0) Start by opening the dataset "MCF7_Filtered_Normalised_3000_Data_test_anonim"
df_DS_MCF7_anon = pd.read_csv("DropSeq/MCF7_Filtered_Normalised_3000_Data_test_anonim.txt",delimiter="\ ",engine='python',index_col=0).T
print("Dataframe dimensions:", np.shape(df_DS_MCF7_anon))
Dataframe dimensions: (5406, 3000)
¶
6.3.1) NN
# Set the model to evaluation mode
model_DS_M.eval()
# Prepare your input data
# Assuming X_new_data is your new dataset in the form of a pandas DataFrame or similar
X_new_tensor_M = torch.tensor(df_DS_MCF7_anon.values, dtype=torch.float32)
# Perform prediction
with torch.no_grad():
new_output1 = model_DS_M(X_new_tensor_M)
# If the model is a binary classifier, apply sigmoid and round the output
new_predictions1 = torch.round(torch.sigmoid(new_output1))
# If you need to convert predictions to a DataFrame or other format
new_predictions1_df = pd.DataFrame(new_predictions1.numpy(), columns=['Prediction'])
# Print or save the predictions
new_predictions1
# new_predictions_df.to_csv('predictions.csv', index=False) # Save to CSV if needed
tensor([[1.],
[0.],
[0.],
...,
[1.],
[0.],
[1.]])
new_predictions_array1 = new_predictions1.numpy()
new_predictions_array_list1=[]
for i in new_predictions_array:
new_predictions_array_list1.append(i[0])
new_predictions_array_list1
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1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 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0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, ...]
# Convert predictions to a DataFrame and save as CSV
new_predictions1_df = pd.DataFrame(new_predictions1, columns=['Predictions'])
new_predictions1_df
| Predictions | |
|---|---|
| 0 | 1.0 |
| 1 | 0.0 |
| 2 | 0.0 |
| 3 | 1.0 |
| 4 | 1.0 |
| ... | ... |
| 5401 | 1.0 |
| 5402 | 0.0 |
| 5403 | 1.0 |
| 5404 | 0.0 |
| 5405 | 1.0 |
5406 rows × 1 columns
¶
6.3.2) CatBoost
predictions_DS_MCF7_catboost = best_catboost_DS_MCF7.predict(df_DS_MCF7_anon)
predictions_DS_MCF7_catboost
array([1, 0, 0, ..., 1, 0, 1], dtype=int64)
# Convert predictions to a DataFrame and save as CSV
predictions_DS_MCF7_catboost_df = pd.DataFrame(predictions_DS_MCF7_catboost, columns=['Predictions'])
predictions_DS_MCF7_catboost_df
| Predictions | |
|---|---|
| 0 | 1 |
| 1 | 0 |
| 2 | 0 |
| 3 | 1 |
| 4 | 1 |
| ... | ... |
| 5401 | 1 |
| 5402 | 0 |
| 5403 | 1 |
| 5404 | 0 |
| 5405 | 1 |
5406 rows × 1 columns
¶
6.3.4) Logistic Regression
predictions_DS_MCF7_logreg = best_logreg_DS_MCF7.predict(df_DS_MCF7_anon)
predictions_DS_MCF7_logreg
array([1, 0, 0, ..., 1, 0, 1])
# Convert predictions to a DataFrame and save as CSV
predictions_DS_MCF7_logreg_DS = pd.DataFrame(predictions_DS_MCF7_logreg, columns=['Predictions'])
predictions_DS_MCF7_logreg_DS
| Predictions | |
|---|---|
| 0 | 1 |
| 1 | 0 |
| 2 | 0 |
| 3 | 1 |
| 4 | 1 |
| ... | ... |
| 5401 | 1 |
| 5402 | 0 |
| 5403 | 1 |
| 5404 | 0 |
| 5405 | 1 |
5406 rows × 1 columns
¶
6.3.5) Final considerations
k=0
for i in range (5406):
if predictions_DS_MCF7_catboost[i]!=new_predictions_array1[i]:
k=k+1
print("Differences CatBoost_DS_MCF7 and SVM_DS_MCF7", k, "\npercentage correspondance CatBoost_DS_MCF7 and SVM_DS_MCF7: ", (5406-k)/5406)
Differences CatBoost_DS_HCC1806 and SVM_DS_HCC1806 95 percentage correspondance CatBoost_DS_HCC1806 and SVM_DS_HCC1806: 0.9824269330373658
k=0
for i in range (5406):
if predictions_DS_MCF7_catboost[i]!=predictions_DS_MCF7_logreg[i]:
k=k+1
print("Differences CatBoost_DS_MCF7 and LogReg_DS_MCF7", k, "\npercentage correspondance CatBoost_DS_MCF7 and LogReg_DS_MCF7: ", (5406-k)/5406)
Differences CatBoost_DS_HCC1806 and LogReg_DS_MCF7 95 percentage correspondance CatBoost_DS_HCC1806 and LogReg_DS_MCF7: 0.9824269330373658
k=0
for i in range (5406):
if new_predictions_array1[i]!=predictions_DS_MCF7_logreg[i]:
k=k+1
print("Differences SVM_DS_MCF7 and LogReg_DS_MCF7", k, "\npercentage correspondance SVM_DS_MCF7 and LogReg_DS_MCF7: ", (5406-k)/5406)
Differences SVM_DS_HCC1806 and LogReg_DS_MCF7 58 percentage correspondance SVM_DS_MCF7 and LogReg_DS_MCF7: 0.9892711801701812
from scipy import stats
# Stack the predictions to create a 2D array (each row corresponds to the predictions for one instance)
stacked_predictions2 = np.vstack((predictions_DS_MCF7_catboost, new_predictions_array1.T, predictions_DS_MCF7_logreg))
# Compute the mode along the columns
final_prediction_DS_MCF7, _ = stats.mode(stacked_predictions2, axis=0)
# Since stats.mode returns a 2D array, we flatten it to get a 1D array
final_prediction_DS_MCF7 = final_prediction_DS_MCF7.flatten()
final_prediction_DS_MCF7
array([1., 0., 0., ..., 1., 0., 1.])
final_prediction_DS_MCF7_df = pd.DataFrame(final_prediction_DS_MCF7, columns=['Predictions'])
final_prediction_DS_MCF7_df.to_csv('final_prediction_DS_MCF7.csv', index=True)
k=0
for i in range(5406):
if final_prediction_DS_MCF7[i]!=predictions_DS_MCF7_catboost[i]:
k=k+1
print("Differences final_prediction_DS_MCF7 and predictions_DS_MCF7_catboost", k, "\npercentage correspondance final_prediction_DS_MCF7 and predictions_DS_MCF7_catboost: ", (5406-k)/5406)
Differences final_prediction_DS_MCF7 and predictions_DS_MCF7_catboost 66 percentage correspondance final_prediction_DS_MCF7 and predictions_DS_MCF7_catboost: 0.9877913429522752
k=0
for i in range(5406):
if final_prediction_DS_MCF7[i]!=new_predictions_array_list1[i]:
k=k+1
print("Differences final_prediction_DS_MCF7 and SVM_DS_MCF7", k, "\npercentage correspondance final_prediction_DS_MCF7 and SVM_DS_MCF7: ", (5406-k)/5406)
Differences final_prediction_DS_MCF7 and SVM_DS_MCF7 29 percentage correspondance final_prediction_DS_MCF7 and SVM_DS_MCF7: 0.9946355900850906
k=0
for i in range(5406):
if final_prediction_DS_MCF7[i]!=predictions_DS_MCF7_logreg[i]:
k=k+1
print("Differences final_prediction_DS_MCF7 and LogReg_DS_MCF7", k, "\npercentage correspondance final_prediction_DS_MCF7 and LogReg_DS_MCF7: ", (5406-k)/5406)
Differences final_prediction_DS_MCF7 and LogReg_DS_MCF7 29 percentage correspondance final_prediction_DS_MCF7 and LogReg_DS_MCF7: 0.9946355900850906
¶
6.4) Prediction on DropSeq HCC1806
¶
6.4.0) Start by opening the dataset "HCC1806_Filtered_Normalised_3000_Data_test_anonim"
df_DS_HCC1806_anon = pd.read_csv("DropSeq/HCC1806_Filtered_Normalised_3000_Data_test_anonim.txt",delimiter="\ ",engine='python',index_col=0).T
print("Dataframe dimensions:", np.shape(df_DS_HCC1806_anon))
Dataframe dimensions: (3671, 3000)
¶
6.4.1) NN
# Set the model to evaluation mode
model_DS_H.eval()
# Prepare your input data
# Assuming X_new_data is your new dataset in the form of a pandas DataFrame or similar
X_new_tensor_H = torch.tensor(df_DS_HCC1806_anon.values, dtype=torch.float32)
# Perform prediction
with torch.no_grad():
new_output2 = model_DS_H(X_new_tensor_H)
# If the model is a binary classifier, apply sigmoid and round the output
new_predictions_DS_HCC2 = torch.round(torch.sigmoid(new_output2))
# If you need to convert predictions to a DataFrame or other format
new_predictions_DS_HCC2_df = pd.DataFrame(new_predictions_DS_HCC2.numpy(), columns=['Prediction'])
# Print or save the predictions
new_predictions_DS_HCC2
# new_predictions_df.to_csv('predictions.csv', index=False) # Save to CSV if needed
tensor([[0.],
[0.],
[0.],
...,
[1.],
[1.],
[0.]])
new_predictions_DS_HCC_array2 = new_predictions_DS_HCC.numpy()
new_predictions_array_DS_HCC_list2=[]
for i in new_predictions_DS_HCC_array2:
new_predictions_array_DS_HCC_list2.append(i[0])
new_predictions_array_DS_HCC_list2
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...]
# Convert predictions to a DataFrame and save as CSV
new_predictions_DS_HCC_df = pd.DataFrame(new_predictions_array_DS_HCC_list2, columns=['Predictions'])
new_predictions_DS_HCC_df
| Predictions | |
|---|---|
| 0 | 0.0 |
| 1 | 0.0 |
| 2 | 0.0 |
| 3 | 1.0 |
| 4 | 1.0 |
| ... | ... |
| 3666 | 0.0 |
| 3667 | 1.0 |
| 3668 | 1.0 |
| 3669 | 1.0 |
| 3670 | 0.0 |
3671 rows × 1 columns
¶
6.4.2) SVM
predictions_DS_HCC1806_svm = best_svm_DS_HCC1806.predict(df_DS_HCC1806_anon)
predictions_DS_HCC1806_svm
array([0, 0, 0, ..., 1, 1, 0])
# Convert predictions to a DataFrame and save as CSV
predictions_DF_HCC1806_svm_df = pd.DataFrame(predictions_DS_HCC1806_svm, columns=['Predictions'])
predictions_DF_HCC1806_svm_df.to_csv('predictions_DF_HCC1806_svm.csv', index=False)
predictions_DF_HCC1806_svm_df
| Predictions | |
|---|---|
| 0 | 0 |
| 1 | 0 |
| 2 | 0 |
| 3 | 1 |
| 4 | 1 |
| ... | ... |
| 3666 | 0 |
| 3667 | 1 |
| 3668 | 1 |
| 3669 | 1 |
| 3670 | 0 |
3671 rows × 1 columns
¶
6.4.3) Logistic Regression
predictions_DS_HCC1806_logreg = best_logreg_DS_HCC1806.predict(df_DS_HCC1806_anon)
predictions_DS_HCC1806_logreg
array([0, 0, 0, ..., 1, 1, 0])
# Convert predictions to a DataFrame and save as CSV
predictions_DS_HCC1806_logreg_df = pd.DataFrame(predictions_DS_HCC1806_logreg, columns=['Predictions'])
predictions_DS_HCC1806_logreg_df
| Predictions | |
|---|---|
| 0 | 0 |
| 1 | 0 |
| 2 | 0 |
| 3 | 1 |
| 4 | 1 |
| ... | ... |
| 3666 | 0 |
| 3667 | 1 |
| 3668 | 1 |
| 3669 | 1 |
| 3670 | 0 |
3671 rows × 1 columns
¶
6.4.4) Final conclusions
k=0
for i in range (3671):
if new_predictions_array_DS_HCC_list2[i]!=predictions_DS_HCC1806_svm[i]:
k=k+1
print("Differences SVM_DS_HCC1806 and Neural_network_DS_HCC1806", k, "\npercentage correspondance SVM_DS_HCC1806 and Neural_network_DS_HCC1806: ", (3671-k)/3671)
Differences SVM_DS_HCC1806 and Neural_network_DS_HCC1806 104 percentage correspondance SVM_DS_HCC1806 and Neural_network_DS_HCC1806: 0.9716698447289567
k=0
for i in range (3671):
if new_predictions_array_DS_HCC_list2[i]!=predictions_DS_HCC1806_logreg[i]:
k=k+1
print("Differences Neural_network_DS_HCC1806 and LogReg_DS_HCC1806", k, "\npercentage correspondance Neural_network_DS_HCC1806 and LogReg_DS_HCC1806: ", (3671-k)/3671)
Differences Neural_network_DS_HCC1806 and LogReg_DS_HCC1806 96 percentage correspondance Neural_network_DS_HCC1806 and LogReg_DS_HCC1806: 0.9738490874421138
k=0
for i in range (3671):
if predictions_DS_HCC1806_logreg[i]!=predictions_DS_HCC1806_svm[i]:
k=k+1
print("Differences SVMt_DS_HCC1806 and LogReg_DS_HCC1806", k, "\npercentage correspondance SVM_DS_HCC1806 and LogReg_DS_HCC1806: ", (3671-k)/3671)
Differences SVMt_DS_HCC1806 and LogReg_DS_HCC1806 130 percentage correspondance SVM_DS_HCC1806 and LogReg_DS_HCC1806: 0.9645873059111959
from scipy import stats
# Stack the predictions to create a 2D array (each row corresponds to the predictions for one instance)
stacked_predictions3 = np.vstack((new_predictions_array_DS_HCC_list2, predictions_DS_HCC1806_logreg, predictions_DS_HCC1806_svm))
# Compute the mode along the columns
final_prediction_DS_HCC1806, _ = stats.mode(stacked_predictions3, axis=0)
# Since stats.mode returns a 2D array, we flatten it to get a 1D array
final_prediction_DS_HCC1806 = final_prediction_DS_HCC1806.flatten()
final_prediction_DS_HCC1806
array([0., 0., 0., ..., 1., 1., 0.])
final_prediction_DS_HCC1806_df = pd.DataFrame(final_prediction_DS_HCC1806, columns=['Predictions'])
final_prediction_DS_HCC1806_df.to_csv('final_prediction_DS_HCC1806.csv', index=True)
k=0
for i in range(3671):
if final_prediction_DS_HCC1806[i]!=new_predictions_array_DS_HCC_list2[i]:
k=k+1
print("Differences final_prediction_DS_HCC1806 and predictions_DS_HCC1806_Neural_network", k, "\npercentage correspondance final_prediction_DS_HCC1806 and predictions_DS_HCC1806_Neural_network: ", (3671-k)/3671)
Differences final_prediction_DS_HCC1806 and predictions_DS_HCC1806_Neural_network 35 percentage correspondance final_prediction_DS_HCC1806 and predictions_DS_HCC1806_Neural_network: 0.9904658131299373
k=0
for i in range(3671):
if final_prediction_DS_HCC1806[i]!=predictions_DS_HCC1806_logreg[i]:
k=k+1
print("Differences final_prediction_DS_HCC1806 and LogReg_DS_HCC1806", k, "\npercentage correspondance final_prediction_DS_HCC1806 and LogReg_DS_HCC1806: ", (3671-k)/3671)
Differences final_prediction_DS_HCC1806 and LogReg_DS_HCC1806 61 percentage correspondance final_prediction_DS_HCC1806 and LogReg_DS_HCC1806: 0.9833832743121765
k=0
for i in range(3671):
if final_prediction_DS_HCC1806[i]!=predictions_DS_HCC1806_svm[i]:
k=k+1
print("Differences final_prediction_DS_HCC1806 and SVM_DS_HCC1806", k, "\npercentage correspondance final_prediction_DS_HCC1806 and SVM_DS_HCC1806: ", (3671-k)/3671)
Differences final_prediction_DS_HCC1806 and SVM_DS_HCC1806 69 percentage correspondance final_prediction_DS_HCC1806 and SVM_DS_HCC1806: 0.9812040315990194